{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "### Answers to Assignment 8 - Time Series and Group Operations\n",
    "\n",
    "Complete the tasks below. Please turn in a single Jupyter notebook named `8_first_last.ipynb` (substitute your first and last name). Please run Kernel > Restart & Run All on your notebook before turning in.\n",
    "\n",
    "#### Precipitation in La Jolla\n",
    "\n",
    "For this assignment, we will use a file containing daily precipitation data in La Jolla from February 2009 to November 2018, which were downloaded from [NOAA](https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:US1CASD0030/detail) using \"standard\" (imperial) units (inches for precipitation, feet for elevation). Download the file from [GitHub](https://github.com/cuttlefishh/python-for-data-analysis/blob/master/data/la_jolla_precip_daily.csv).\n",
    "\n",
    "#### A. Set up the file\n",
    "\n",
    "1. Import the CSV file as a Pandas DataFrame with default header and index.\n",
    "2. Change the 'DATE' column to timestamps using `pd.datetime()`. Alternatively, import the csv file with `parse_dates` set to the columns you want to parse as datetime.\n",
    "3. What was the maximum daily precipitation (in inches) during this time period and when was it?\n",
    "\n",
    "#### B. Explore the dataset\n",
    "\n",
    "1. We don't need the columns 'SNOW' and 'SNOW_ATTRIBUTES' because there was no recorded snow in the dataset. Delete those columns \"in place\".\n",
    "2. Find out about the sampling stations. Notice that the column values are similar between rows except 'DATE' and 'PRCP'. Explore these other columns using three different commands (we haven't covered them yet, but they are easy to use and you can always google them): \n",
    "    - Use the `value_counts()` method for each of these columns: 'STATION', 'NAME', 'LATITUDE', 'LONGITUDE', and 'ELEVATION'. There should only be one cateogory for each calculation because all the data come from the same station. To see what output looks like for a more diverse series, use the `value_counts()` method on 'PRCP' and 'PRCP_ATTRIBUTES'.\n",
    "    - Make a DataFrame with just the columns 'STATION', 'NAME', 'LATITUDE', 'LONGITUDE', and 'ELEVATION' and use the method `drop_duplicates()` to see all the unique combinations of values in those five columns. \n",
    "    - Create a groupby object using `groupby('STATION')`, then use the `count()` method on that groupby object to count the number of values for each station.\n",
    "3. Create columns for 'YEAR', 'MONTH', 'DAY', and 'DAY_OF_YEAR'. Hint: use attributes such as the `.year` attribute, or use a regular expression, with a list comprehension.\n",
    "\n",
    "#### C. Plot precipitation versus time\n",
    "\n",
    "1. Use the Matplotlib function `plot()` to plot precipitation versus date as a single line, using a single color from ColorBrewer or xkcd (via Seaborn). Save your plot as a PDF file.\n",
    "2. Plot precipitation versus day of year for each year separately, with each year as a different colored line, the x-axis going from the beginning to the end of the year, and a legend indicating the year. Hint: use the 'DAY_OF_YEAR' and 'YEAR' columns you created in B3. Color using a qualitative color palette from Seaborn or ColorBrewer (via Seaborn). Save your plot as a PDF file.\n",
    "3. Create a set of subplots with a grid of 2 columns and enough rows to have one subplot per year (e.g., to show 10 years, your set of subplots would be 5 by 2). Plot precipitation versus day of year with each year as a separate subplot. Save your figure as a PDF file.\n",
    "\n",
    "#### D. Plot distributions of the precipitation data\n",
    "\n",
    "1. Plot a histogram of precipitation values using the Matplotlib function `hist()`.\n",
    "2. Plot a histogram with kernel density and rugplot with the Seaborn function `distplot()`. Play around with the settings to make a histogram that represents the data well.\n",
    "3. Use groupby to group the data by year or by month. Which year was the rainiest? Which month was the rainiest?\n",
    "4. Make boxplots by year and by month using the Seaborn function `boxplot()`. Hint: If you make a boxplot of your DataFrame without grouping, the boxplots will be centered on zero, because there are so many days with zero precipitation. Instead, use groupby to group the data by year *and* month (use a list containing these columns), set `as_index=False`, average over those groups, and save this as a new DataFrame; then use this for your boxplots.\n",
    "\n",
    "#### E. Pivoting, stacking and unstacking\n",
    "\n",
    "1. Use `pivot_table()` to produce a new DataFrame, where rows=years and columns=months, containing the mean precipitation of each month.\n",
    "2. Draw a heatmap of years x months where each square is a month colored by mean precipitation. Adjust the colormap to highlight months with heavy precipitation. Hint: Seaborn's `heatmap()` function makes this very easy.\n",
    "3. Stack the monthly precipitation table using `stack()`. View the values for 2017. Which month had the most precipitation in 2017? Describe the distribution statistics for all months using `describe()`. How many months are included in this dataset? What was the median month (daily average) value in this time period?\n",
    "4. Use `unstack()` to stack precipitation pivot table by month and then year. View the values for December. Which year had the wettest December?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sns.set(context='notebook', style='whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_palette(sns.color_palette('Paired', 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer to Question A - Set up the file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A1. Import the csv file as a Pandas DataFrame with default header and index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# By default, the index will start with zero and the header will be the first row of the csv file.\n",
    "df = pd.read_csv('../../data/la_jolla_precip_daily.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>NAME</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>ELEVATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>PRCP_ATTRIBUTES</th>\n",
       "      <th>SNOW</th>\n",
       "      <th>SNOW_ATTRIBUTES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>2009-02-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>2009-02-19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>2009-02-20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>2009-02-21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>2009-02-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       STATION                    NAME  LATITUDE  LONGITUDE  ELEVATION  \\\n",
       "0  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "1  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "2  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "3  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "4  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "\n",
       "         DATE  PRCP PRCP_ATTRIBUTES  SNOW SNOW_ATTRIBUTES  \n",
       "0  2009-02-18   0.0             ,,N   0.0             ,,N  \n",
       "1  2009-02-19   0.0             ,,N   0.0             ,,N  \n",
       "2  2009-02-20   0.0             ,,N   0.0             ,,N  \n",
       "3  2009-02-21   0.0             ,,N   0.0             ,,N  \n",
       "4  2009-02-22   0.0             ,,N   0.0             ,,N  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It's always good to take a quick look at your DataFrame.\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATION             object\n",
       "NAME                object\n",
       "LATITUDE           float64\n",
       "LONGITUDE          float64\n",
       "ELEVATION          float64\n",
       "DATE                object\n",
       "PRCP               float64\n",
       "PRCP_ATTRIBUTES     object\n",
       "SNOW               float64\n",
       "SNOW_ATTRIBUTES     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check dtypes\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A2. Change the 'DATE' column to timestamps using `pd.datetime()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['DATE'] = pd.to_datetime(df['DATE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATION                    object\n",
       "NAME                       object\n",
       "LATITUDE                  float64\n",
       "LONGITUDE                 float64\n",
       "ELEVATION                 float64\n",
       "DATE               datetime64[ns]\n",
       "PRCP                      float64\n",
       "PRCP_ATTRIBUTES            object\n",
       "SNOW                      float64\n",
       "SNOW_ATTRIBUTES            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check dtypes again\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, import the csv file with `parse_dates` set to the columns you want to parse as datetime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../../data/la_jolla_precip_daily.csv', parse_dates=['DATE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A3. What was the maximum daily precipitation (in inches) during this time period and when was it?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.35"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PRCP'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>The maximum rainfall over this period was 3.35 inches.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>NAME</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>ELEVATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>PRCP_ATTRIBUTES</th>\n",
       "      <th>SNOW</th>\n",
       "      <th>SNOW_ATTRIBUTES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>2010-12-22</td>\n",
       "      <td>3.35</td>\n",
       "      <td>,,N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "         STATION                    NAME  LATITUDE  LONGITUDE  ELEVATION  \\\n",
       "663  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "\n",
       "          DATE  PRCP PRCP_ATTRIBUTES  SNOW SNOW_ATTRIBUTES  \n",
       "663 2010-12-22  3.35             ,,N   NaN             NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One way to get information about the maximum precipitation date.\n",
    "df[df['PRCP'] == df['PRCP'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATION                       US1CASD0030\n",
       "NAME               LA JOLLA 2.1 SE, CA US\n",
       "LATITUDE                          32.8257\n",
       "LONGITUDE                         -117.25\n",
       "ELEVATION                           149.7\n",
       "DATE                  2010-12-22 00:00:00\n",
       "PRCP                                 3.35\n",
       "PRCP_ATTRIBUTES                       ,,N\n",
       "SNOW                                  NaN\n",
       "SNOW_ATTRIBUTES                       NaN\n",
       "Name: 663, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Another way to get information about the max precipitation value.\n",
    "df.loc[df['PRCP'].idxmax()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>The max precipitation occurred on Dec 22, 2010.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer to Question B - Explore the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B1. We don't need the columns 'SNOW' and 'SNOW_ATTRIBUTES' because there was no recorded snow in the dataset. Delete those columns \"in place\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete these columns because there wasn't any recorded snow.\n",
    "df.drop(['SNOW', 'SNOW_ATTRIBUTES'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "      <th>2</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
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       "      <th>3</th>\n",
       "      <td>US1CASD0030</td>\n",
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       "      <td>32.8257</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>2009-02-22</td>\n",
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      "text/plain": [
       "       STATION                    NAME  LATITUDE  LONGITUDE  ELEVATION  \\\n",
       "0  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "1  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "2  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "3  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "4  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "\n",
       "        DATE  PRCP PRCP_ATTRIBUTES  \n",
       "0 2009-02-18   0.0             ,,N  \n",
       "1 2009-02-19   0.0             ,,N  \n",
       "2 2009-02-20   0.0             ,,N  \n",
       "3 2009-02-21   0.0             ,,N  \n",
       "4 2009-02-22   0.0             ,,N  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the DataFrame again.\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B2. Find out about the sampling stations. Notice that the column values are similar between rows except 'DATE' and 'PRCP'. Explore these other columns using three different commands (we haven't covered them yet, but they are easy to use and you can always google them): "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `value_counts()` method for each of these columns: 'STATION', 'NAME', 'LATITUDE', 'LONGITUDE', and 'ELEVATION'. There should only be one cateogory for each calculation because all the data come from the same station. To see what output looks like for a more diverse series, use the `value_counts()` method on 'PRCP' and 'PRCP_ATTRIBUTES'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# These columns refer to the stations where data was collected.\n",
    "columns = ['STATION', 'NAME', 'LATITUDE', 'LONGITUDE', 'ELEVATION']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "US1CASD0030    3514\n",
      "Name: STATION, dtype: int64 \n",
      "\n",
      "LA JOLLA 2.1 SE, CA US    3514\n",
      "Name: NAME, dtype: int64 \n",
      "\n",
      "32.8257    3514\n",
      "Name: LATITUDE, dtype: int64 \n",
      "\n",
      "-117.2501    3514\n",
      "Name: LONGITUDE, dtype: int64 \n",
      "\n",
      "149.7    3514\n",
      "Name: ELEVATION, dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Count the times each value was observed in each column.\n",
    "for column in columns:\n",
    "    print(df[column].value_counts(), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00    3011\n",
      "0.01     110\n",
      "0.02      68\n",
      "0.03      38\n",
      "0.04      25\n",
      "0.07      20\n",
      "0.05      16\n",
      "0.08      15\n",
      "0.06      12\n",
      "0.12       9\n",
      "0.16       8\n",
      "0.09       8\n",
      "0.25       8\n",
      "0.13       7\n",
      "0.17       6\n",
      "0.10       6\n",
      "0.34       6\n",
      "0.63       5\n",
      "0.14       5\n",
      "0.24       5\n",
      "0.18       4\n",
      "0.20       4\n",
      "0.35       4\n",
      "0.30       4\n",
      "0.15       4\n",
      "0.27       4\n",
      "0.38       4\n",
      "0.19       3\n",
      "0.41       3\n",
      "0.11       3\n",
      "        ... \n",
      "0.76       1\n",
      "1.50       1\n",
      "2.00       1\n",
      "1.18       1\n",
      "3.35       1\n",
      "1.23       1\n",
      "0.48       1\n",
      "1.34       1\n",
      "0.55       1\n",
      "2.02       1\n",
      "0.83       1\n",
      "0.46       1\n",
      "0.87       1\n",
      "0.40       1\n",
      "1.37       1\n",
      "0.84       1\n",
      "0.51       1\n",
      "0.58       1\n",
      "0.36       1\n",
      "1.55       1\n",
      "0.81       1\n",
      "0.93       1\n",
      "0.64       1\n",
      "1.10       1\n",
      "0.54       1\n",
      "0.67       1\n",
      "2.45       1\n",
      "0.47       1\n",
      "0.26       1\n",
      "0.23       1\n",
      "Name: PRCP, Length: 89, dtype: int64 \n",
      "\n",
      ",,N     3245\n",
      "T,,N     269\n",
      "Name: PRCP_ATTRIBUTES, dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for column in ['PRCP', 'PRCP_ATTRIBUTES']:\n",
    "    print(df[column].value_counts(), '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>The results are pretty boring when all columns have the same values. The last two examples have more variation.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a DataFrame with just the columns 'STATION', 'NAME', 'LATITUDE', 'LONGITUDE', and 'ELEVATION' and use the method `drop_duplicates()` to see all the unique combinations of values in those five columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>NAME</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>ELEVATION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       STATION                    NAME  LATITUDE  LONGITUDE  ELEVATION\n",
       "0  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[columns].drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>This shows the unique combinations of these five columns. All rows have the same values.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a groupby object using `groupby('STATION')`, then use the `count()` method on that groupby object to count the number of values for each station."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>ELEVATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>PRCP_ATTRIBUTES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATION</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>US1CASD0030</th>\n",
       "      <td>3514</td>\n",
       "      <td>3514</td>\n",
       "      <td>3514</td>\n",
       "      <td>3514</td>\n",
       "      <td>3514</td>\n",
       "      <td>3514</td>\n",
       "      <td>3514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             NAME  LATITUDE  LONGITUDE  ELEVATION  DATE  PRCP  PRCP_ATTRIBUTES\n",
       "STATION                                                                       \n",
       "US1CASD0030  3514      3514       3514       3514  3514  3514             3514"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_station = df.groupby('STATION')\n",
    "grouped_station.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>Groupby combined with count gives us information about the number of values in each column for a given value of 'STATION'. Here they are all the same.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B3. Create columns for 'YEAR', 'MONTH', 'DAY', and 'DAY_OF_YEAR'. Hint: use attributes such as the `.year` attribute, or use a regular expression, with a list comprehension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can add columns for year, month, day, and day of year.\n",
    "df['YEAR'] = [x.year for x in df.DATE]\n",
    "df['MONTH'] = [x.month for x in df.DATE]\n",
    "df['DAY'] = [x.day for x in df.DATE]\n",
    "df['DAY_OF_YEAR'] = [x.dayofyear for x in df.DATE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>NAME</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>ELEVATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>PRCP_ATTRIBUTES</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>2009-02-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>2009-02-19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>2009-02-20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>2009-02-21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>2009-02-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       STATION                    NAME  LATITUDE  LONGITUDE  ELEVATION  \\\n",
       "0  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "1  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "2  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "3  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "4  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "\n",
       "        DATE  PRCP PRCP_ATTRIBUTES  YEAR  MONTH  DAY  DAY_OF_YEAR  \n",
       "0 2009-02-18   0.0             ,,N  2009      2   18           49  \n",
       "1 2009-02-19   0.0             ,,N  2009      2   19           50  \n",
       "2 2009-02-20   0.0             ,,N  2009      2   20           51  \n",
       "3 2009-02-21   0.0             ,,N  2009      2   21           52  \n",
       "4 2009-02-22   0.0             ,,N  2009      2   22           53  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>These extra columns of date information will come in handy below.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer to Question C - Plot precipitation versus time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C1. Use the Matplotlib function `plot()` to plot precipitation versus date as a single line, using a single color from ColorBrewer or xkcd (via Seaborn). Save your plot as a PDF file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(6,5))\n",
    "\n",
    "title = 'Daily precipitation in La Jolla'\n",
    "color = sns.xkcd_rgb['indigo']\n",
    "\n",
    "ax.plot(df['DATE'], df['PRCP'], color)\n",
    "ax.set_title(title)\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Precipitation (inches)')\n",
    "\n",
    "fig.savefig('precipitation_single_line.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>This is a basic Matplotlib plot, with subplots to provide figure and axis handles, and Seaborn for the color.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C2. Plot precipitation versus day of year for each year separately, with each year as a different colored line, the x-axis going from the beginning to the end of the year, and a legend indicating the year. Hint: use the 'DAY_OF_YEAR' and 'YEAR' columns you created in B3. Color using a qualitative color palette from Seaborn or ColorBrewer (via Seaborn). Save your plot as a PDF file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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33oDT6cSIESPw0ksvaY7JzMzE4MGDMXbsWM377777LsxmMyZNmhQ1+Sl1QH3rcKenJIqLi3HgwAG/+8jolVkuKirS7JOfn49ly5Zh9OjRmDt3LpxOZ22ugUIxHOHqR1FVVYXp06djxYoVdXEZlGij1AQ0lqIIGMz+7LPPkJeXhxkzZmDs2LFo1qwZhg0bhsmTJ/s9Tq/M8urVq5X3bDYb+vTpgylTpqBz586YNm0ali5d6jXDolBqw+8nc3Du5CbltVxYLxx07pmOG3v6LzOunigB8OpHAUDpR5GSkqL0o2jbtq3mPDt27ECXLl3w1FNPhUV2itGpZ64nmXXr1uHDDz/Etm3b8Oc//xmzZs3CQw89FFBRyJw8eRLPPvssMjMz0aVLF+X9+Ph4LF++XHn99NNPY/r06SEpCnXt/lDJy8ur9bHRoj7ICBhHTovForQKdTqd4Hles93zdW1xOp0BW5Jef/31AMQJ0e+//44tW7bgkUceQevWrZVjW7RogaKiIthsNjz66KMAgEOHDmnOL7tqP/jgA/A8H3IrVJfLFZHvxyjfeSDqg5xqGZtevYIYAGfOnAZbYxzZAyoKhmHQtm1b7NmzByNGjIDFYoEgBLcYxLPMspqioiLk5uZi3LhxAMS0MLlBS7AkJSUhNjY2pGNkuQYOHBjycdGkPsgIGEvOgoICpZR4z6QH0DPpAWVbXZQZB8SJ0vPPP4+pU6fCbDajsLBQkSM2NhYWi0V5bbPZYLFYEBsb6yWrbJmEeg0xMTFeySjXipG+c3/UBzk9Zbx0oRVsJUDXLl3Q/Jbwy+50Oms1wQ644C4mJgbLly/Hvn37cOedd+Kzzz5DkyZNAp5Yr8yymri4OCxYsADnz58HIQSrVq3SBLop+hCeIP8dO8qPcXUtCiUAeXl5ePLJJzF58mSMGTMGiYmJIfWjoDRC6lt6rMy8efPw0Ucf4a233kLLli2Rl5eHrKysgCf2VWZ5586dyMjIwK233oq5c+di4sSJYFkWAwYMoH7YIGBtBM4rBOc2udDqZlr816jIE6VFixbhD3/4AwBtP4pOnTohJycHDzzwQIAzURoV9XHBHQB069YNr7zyCs6dOwdCCLKysoKyKHyVWZZ9sQAwfPhwpXE8JTRoR0xjE65+FJTGhjE73AVUFIcOHcILL7wAi8WCNWvWID09He+//z4GDBgQDfkonhjr/qH4IFz9KGTo+onGgVFLeASMUcyfPx///ve/0apVKyQmJmL+/PmYN29eNGSjUCiUxkV9VRQOhwM9evRQXqekpIQtzZBCoVAoaowZzA6oKCwWCyoqKsBITvHTp09HXChKENAYBYXS8DBoCY+AMYqJEyfi8ccfR2lpKf7xj3/gxx9/xNy5c6MhG0UPY90/FAolIhjrQQ+oKIYOHYpu3brhxx9/hCAIeP7559G9e/doyEbxB7UoKJQGh9LZzlh6IrjGRXFxcejXrx8IIXA4HDh69KjfgoAUCoVCqQWSy8lorVADKopFixZhxYoVmmJlDMNgx44dERWMoo/BXJcUP4SjzHheXh7eeOMNsCyLVq1a4fXXX0fHjh2jfi2UaGHMYHZARbF582bs3LnTq6olpY4w1kSD4gO9fiw5OTlYuHAhVq5ciQ4dOuDZZ5/Frl27kJKSguLiYsyYMQP79+/H4MGDlfNMmTIFS5cuRe/evbFu3TpkZWXh/fffr8Mro0QUg5bwCJj11Lp1a6okDIR8H9GV2cZGrx+Lusy4xWJRyowDUMqMjxgxQjmHy+XCiy++iN69ewMAevXqhYsXL9bJ9VCihLKOom7F8MSnRSF3r7v55puRlZWF0aNHa6q70hhFHWGwG8io5J3LwYEz7n4UgsDDZApPP4pBXdMxsLP/fhR6/Vgef/xxtGvXTnm/ffv2KC4uBgA888wzsNlsmsqeMTExSE9Pl+QXkJ2djXvuuScs10AxJqS+uZ48Swbs3LlT+Z3GKOoOxTKlFkW9QN2PxWw24+zZs8o2QoiyPskfLpcL06ZNA8dxePbZZyMoLaXOqW/BbLVikGv5O51OVFdX47rrrouKcBQdjDXRMCwDO4/SzPrroh+FZz+Wffv2hVxm3GazYeLEiWjVqhXef/99WK3WSItNqUvqawmPLVu2YMyYMQDEZkOjRo3SKBFKlDHW/UPxgV4/FnWZcZ7nkZOTgyFDhvg9j9wq+N1331WaF1EaMsayJGQCZj198MEH+OSTTwAAXbt2xfr16/Hcc8/h7rvvjrhwFG+U6pLU9WRowlFm/NixY9ixYwd69OihTNbat2+vaSFMaWAY1KIIqCgEQUBiYqLyukOHDkG3QqVEAGPdPxQfhKPM+M0334zjx4+HXTZKfcBYD3pA11ObNm2wZs0acBwHnuexbt06mi5bh1CDgkJpuLhLeBhrMh5QUcydOxdr165F37590bdvX6xduxazZ8+OhmwUPYw10aBQKOFEyXqqYzk8COh66tKlC9avX4+KigqYzWY0a9YsGnJRfEHTYymUBkw9W0chU1paijVr1qC8vFzzvi//KyWyGG2mQaFQwohBS3gEVBRTpkxBXFwcbr755qAWB1EijLHuHwqFEk7qa9bTpUuXlAqYoaJXPVNNQUEBZsyYAZvNhkGDBmHOnDmaMiEUbwx2/1AolDBCDNrhLmAw+/rrr4fdbg/5xOrqmRs3bsTRo0fx9ddfa/aZMmUKZs2ahe3bt4MQgrVr14b8OQ0N3kFw5WfW9w40RkGhNAKMpSgCTt/bt2+P+++/H7fffjvi4uKU9wPFKNTVMwGge/fuKCoqUrYXFhbC4XCgX79+AICxY8diyZIlGD9+fK0upKFQdozDuc9dwAgfX42x7h+KH8LRj+LAgQN4/fXXwbIsOnbsiLfeegstW7aM+rVQao9QWAymXRswMcGUX6mnrqeOHTvWqlGKXvXM1atXK++VlJRoKmm2a9dOqaQZLOpKm6GSl5dX62MjypmWADoAAqMvY0lTADfC6XAgL68g2tLpYpS/pcVigc1m87nd37Zw89NPP+H777/HqlWrAAAvvPACPv/8cyxZsgTLly9HQkICMjIy8NVXX+HOO+/E5cuXkZWVhf3796N///6KrFOnTsW7776Lbt26YcmSJXj//fe9Cnb6w+VyReT7Mcp3Hoi6lpPhePT+3y5cGtQLZT31x1G1jM3tNpgBFBYV4rSB/sYBFcULL7xwTR+grp7ZpUsX5X1BEDTB8WAraapJSkpCbGxsyDLl5eVh4MCBIR8XDS7zLH4/6AIAXRkrT3I4uduJuCZxuMUA12Ckv2VBQYFS+C/n94P44nf3gybwPEzm8JQZv+/GgRh1Y3+/+9xwww2YPn06WrVqBQC46aabcPHiRXTp0gU33XQTAGDMmDH49ttvMWzYMKxevRp/+tOf0LZtW8TGxirXsW3bNlitVrAsi6tXr6JXr14hFTeMiYlBcnJyLa9UHyN95/4wgpzEVgPnf7/DjYmJ6KYji6eM53+Jg6sauL5DB7SJgOxOp7NWE2yfiuLRRx/F6tWr0b9/f90B/Oeffw54cs/qmWoSExM1lTRLS0sDVtJsFATIjjOYRUrxQTj6UQCA1WrF8ePH8dRTT8FiseAf//hHdC6AEh5CbESk7GawB92noli8eDEAICcnp1YnlqtnLlq0CH/4wx+8tnfs2BGxsbGKRt20aVPASpqNgYBp1Ma6fwzLqBv7a2b9dVFmHAhPP4pevXohNzcXa9aswUsvvYQ1a9ZEUGJKRAh24FdKdxjrQfeZ9bRr1y4A7hiF5z8A+O9//+vzxOrqmenp6UhPT8fq1asxYcIEHDlyBACwcOFCvPHGG0hNTYXdbscTTzwRzmurnyj3h/4AQhsX1R/y8vLw5JNPYvLkyRgzZoyXFR2oH4XT6cQ333yjvL7vvvtokcD6RqjrIurbOgqO4/Dwww/jvvvuw9ChQ3H99dcDELOVvv/+e6xbt04pfayHr+qZjz76qPK73DCeooJaFA0CPYta3Y+iU6dOyMnJwQMPPODzHBaLBXPmzEFiYiKSkpKwdetWDBgwIFqXQAkHtVUUBnvQ/cYoUlJSsGzZMmRnZ6OqqgoA0KJFCwwbNgyLFy9Gp06doiZoY4G6nhoG4ehHYTabsWjRIsyaNQs8zyMhIQHz5s2LhviUcCGEaiHIRQGN9aD7zXq6/vrrMWfOHMyZMwdlZWVgGEbJ4qBEmgCuJ4qhCUc/CgAYNGgQ1q9fH1bZKFEkxGB2vbMoPGndunUk5aDIBGtR0BgFhWJ8QnQ9EdTTEh6U6EICzEAMdv9QKBQ/yM9z0K4k4vWLIaCKwmgoFoUPk4F2uKNQ6g8hZzGF6KqKElRRGIyA95PBbiAKheKHkLOeBGl3Y7VCDRij+Oabb/D666+joqIChBBlkVAwK7MptSDYldm0NwiFYnxCjU3X12D2ggULMG3aNNq4KFoEWHBHgxQUSj0i1GC2QRfcBXQ9yesmOnXq5LUymxJ+Ak0oDHb/UPyQnZ2NtLQ0pKWlYf78+QDEPi2jR4/GsGHDsGjRIq9jMjMzddNhjx07hqSkpIjLTAkztY1RGIyAiiI5OVkp50GJAoEsT9l1SY07Q6PXuCsnJwfTp0/H0qVLsWXLFuTn5yvPVnFxMV588UVs377d61w1NTV47bXXwLJ+GlpRjElDL+Ehs2vXLnz66aewWq2wWq00RhFpgq31RDE0eo27zp49i86dO+OGG24AAIwePRrbtm1DSkoKNm/erJQZ9+TNN9/EX//6V/rM1UdqbVHUs2D2v//97yiIQZGhJTzCw5Zzp7H53CnlNc/zMIepH8Xozt0xsnM3v/uEq8z4jh074HA4/Jb6oBiYxmJRdOzYEVu2bMEPP/wAlmVx11134f7774+GbI2TIBUFzSuoH1xLmfHLly/j/fffp5O1+kyo/Sjk9NgIiVNbAiqKjz76CF988QXGjBkDQghWrFiBoqIiPPfcc9GQr/FBXU9hYWTnbppZf130o/Bs3LVv376Qyox/9913KC8vx2OPPaa8l56ejlWrVqFZs2YRlZ0SJmq94M5YD3pARbFx40asXr1auTHHjRuHhx56iCqKCEFdTw2DcJQZf/DBB/Hggw8qr3v16oVNmzZFXHZKGFH6EIVYwqO+LbgDoJm9NG/eHBZL0LUEKbUl4IK7qElCqQXhKDNOaQjUbmW20WaEQcUo/vOf/2D8+PEAgFWrVilNjCgRIJAmMNb9Q/FBuMqMq6Hd7eohjaXW05w5c/DNN9+gX79+6NevH7766ivMmjUrGrI1SqjriUJpQDT0DncyCQkJWLlyJWpqaiAIQp00qG9UBFyZbawbiEKh+KGWJTyM9pz7VBTz5s3DjBkz8Le//U13+wcffBAxoRoz7vsjgOuJxigoFOMjhOpKkvevJ8FsOVNj+PDh1/QB1dXVeOSRR/DBBx949djOzs7G559/jhYtWgAAHnroIU0qYLRhvy0BeALrPQl1JkPQ1WMpFIrhCb1xkbEUhIxPRXH33XcDAM6dO4e///3vmm1ZWVkYM2ZMwJP/8ssvmDlzpmaRkZr8/Hy888476N+/fwgiRw529XkAMIai8IV0H9EFdxRKPUCZ+IU6wzPWjNCnoliyZAkqKyuxZcsWVFdXK++zLIvdu3f7zOhQs3btWsyePRuZmZm62/Pz87Fs2TIUFhbitttuw9SpUxEbG1uLy2g40GA2hdKAaCAlPHxmPSUnJ6NVq1YwmUxo1aqV8i8xMRELFy4M6uTz5s3DoEGDdLfZbDb06dMHU6ZMwYYNG1BZWYmlS5fW7iqiTNmej3H1hwjJGqAVqsHuHwqF4o+Qg9nGXEfBkADOs8OHD6Nv377X9CF33303PvnkE68YhZpjx45h+vTp2LhxY8DzOZ1Or+Jp4aDPMvFnwbP+92u14/8BAMr//GHYZcDPicDZVsCgIuDGSu/tx9oCv7YFWtcAQ8+F//PrMRaLBT169KhrMRSWLVuGr7/+GgBw11134e9//zt++uknvP3223A6nRg2bBief/55zTGvvPIKbrvtNtx3330AgM2bN+O9995DmzZtlPO88MILQcvw22+/geO4MF0RJVSaFV1B528PoaJzAi7cFbifSMudfwNDBLgSB8N+y9MRkyspKSksKDg5AAAgAElEQVQk703A9NgWLVogKysLdrsdhBAIgoBz585hzZo11yRoUVERcnNzMW7cOABisCfUFd+hXqxMXl4eBg4c6PW+HXkAoLtNzakdCGq/2nD2nBNXznIA0T9/4VUXLv3Koml8PPpE4PNDxdffsi4oKCjwmb4d7VpPubm52LdvHzZt2gSGYfDMM8/g22+/xcKFC7Fy5Up06NABzz77LA4cOICUlBQUFxdjxowZ2L9/P+666y5F1pMnT+Lll1/GqFGjaiVHTEwMkpOTw3lphvrO/WEEOfkmp8B+ewitW7dCgo4snjKe2il6Etq0aROR57u2k+yAI/PkyZORlJSEgwcPIi0tDd9++y1uueWWWgmpJi4uDgsWLMAdd9yBTp06YdWqVbj33nuv+bz1ngCup5B78DZStpwtQc6ZEuV1OMuMj+raHiO7+C7mB4SvH8WRI0dw9uxZLFu2DL169cIrr7yCli1bhuU6KFEgxOqxRi3hEXBlts1mw5w5c3DXXXdhyJAhWLFiBQ4dOlTrD5wwYQKOHDmCNm3aYO7cuZg4cSJSU1NBCMFTTz1V6/M2GALcHwZduEnxoGfPnujXrx8Adz8KhmH89qPQyyRs164dnnvuOXzxxRfo0KED5s6dG50LoISHxlI9tlWrVgCAzp074+TJk+jbt6/fGvp67Ny5U/l9+fLlyu/Dhw+/5nUaDY1gs54Mdh8ZjpFdtLP+uigzDlxbPwoA+Oc//6n8/swzz1Cru74RQnqsNlxsrAc8oEXRuXNnzJs3DwMGDMCnn36KlStX0uCYBBEi8HdQZiC+XE/GuoEovsnLy8OTTz6JyZMnY8yYMUhMTAypH0VVVZWmaREhJGzuM0qUCMmiUO1jsOc8oKJ49dVXMWjQINx888148MEHsXfvXrz22mvRkM3wCGxN5E4eaGW2se4jigdyP4qFCxciLS0NgLYfBc/zyMnJwZAhQ3yeo2nTpvjXv/6FX375BQDw6aefUouivhFAUQgEcLLe+9SbWk8yy5YtU1Zmjx8/HuPHj0dWVhYGDBgQceGMDnE5gNjm4T1noPuj1is9KdEkHP0ozGYz3n33Xbz66qtwOBzo0qUL5s+fHw3xKeEigKLYdaYjPj1kx+Jn4j3Kdxjr+Y7oyuyGjsDaw3/SIBfcUT1hbMLVj2LQoEHYsGFDWGVrzPxcUoG3D57Bx/f0Raw5oEPl2vGR9XTm8s+4vlUvnK/wNdE01gPuU1EkJyfjyJEjyspsGbPZHPTK7IYOiYDrKaACMGbNMAqlXnCqwo7TFXZUuTjENomJ/AfqzOxsznIs2/UMenf4PwBelzYT7cNvLD3hW1GkpKQgJSUFQ4YMueaV2Q2ViMQogq31ZLAbiUKpDwjSYCxEyyTXURQc7wQAFJX9qrzHC4BJ7XoyWBXZgP0ofNVfasj9KIJJWwQA4oqkoqCuJwol3MjtIYRoPT96MUWdsYXjgRjGuOmxEe9HYXSqagg++saBx4bEQsmyJwiqMVAkLIqgg9kUCiVkjGBRMNLgQlQPMycAMaZ6mPUk96MYM2YMysrKcODAAZhMJtx+++1o3jy8mT51yW8XeZwpFrB+rwt/kd/kCWDy1hRs5UVUHlqvvI5EjCLoxkXGuo8olHqBrCD4KA3ExEcwW7MNAMcTgDHuQx0w7P/1119j2LBh+M9//oN//etfuPfee7F3795oyBYVmsaKCuHXC7z7TR92aUnOKyjf+7F7tzrIeqIxCgql9vChdia9VgTvDncCkcca93u84CFVfYlRyCxatAiffvopevXqBQA4evQoZs6c2WBS9ohiiqre5PVvI8KzmteCyxEBeQLtEOR+lDonOzsbW7duBSAmh2RmZiI3NxdvvPEGnE4nRowYgZdeeklzTGZmJgYPHoyxY8cCAEpKSjBz5kyUlJQgLi4OCxcu9Fuun+If2d1Tl64nQVICBASM9D/He7ibDPaAB7Qo4uLiFCUBALfcckvItZ6MjK7xwOu8B4Axacsn1KnriWJocnNzsXv3bmzYsAEbN27E0aNHkZOTg+nTp2Pp0qXYsmUL8vPzsWvXLgBAcXExXnzxRWzfvl1znszMTAwdOhQbN25Eeno6TU2/RuTnPVquJz1FoTQnIgSM5G7ieALtQ2+sBz2gohgyZAg+/PBD2O12OJ1O/Pe//0XPnj1RUVGB8vLyaMgYUXQVha+UCJPWAItsemyAWk/Guo8oHqjLjFutVq8y4xaLRSkzDkApMz5ixAjlHFevXsWvv/6KRx55BADwwAMPePWvp4SGrCCiNuHyoygIAJOiKKIpVOgEdD0tX74cPM/jnXfe0bwvN2QpKCiImHDRQO+7IQLRTXrytijCH6MIlO1A02ODY99JFj8ddxdt5AXAbAqPYr+jlwW397T63adnz57K73KZ8ccff9xvmXGbzaZpKnP+/Hlcf/31ePPNN3HgwAG0a9cOr7zySliuobES7WC2gurj3DEKd84MJ0ATl6g3WU8yR48ejYYcdYagFzPyEaOApCgIiQXAgPCRqB7r8TPU7RRDcS1lxjmOw7FjxzBp0iS8/PLL+N///odp06Zh5cqVUZC8YRL1dRSCjkWheogZtUWhpr4Eszdt2oT09HSsWLFCd3tDaTKke7/4uIsYRvTUVVRuBhCH5sJbEZOLpsdeG7f3tGpm/WI/iiZRlSEvLw8ZGRmYPn060tLSsG/fvpDKjLdr1w7x8fEYOnQoAGDUqFHIysqKuNwNGSOsoyCSRSEQQfH9cwJxxy7EvaIjX5D4VBTnzp0DAJw4cSJqwtQF+haFj52VGEWcdHD4LQr3/eQ/PdZglinFA7nM+KJFi5TFq+oy4506dUJOTg4eeOABn+e48cYbkZiYiF27diElJSVsbYgbM25FEaUP9JP15GVR1MdaTxkZGQCAN954A/v378dtt92G8vJyHDhwAPfcc0/UBIw0+llPPiwKzxiF4EujXAOBLAZjWaQUH4SjzDgAvPfee5g9ezYWLFiAZs2aeVWXpYQGr7ie6s6iKHc6wcMCQtzBbJ4HjJz1FNQ6ip9//hkrV66Ew+HAhx9+iBMnTuC5556LhnwRRzdo5Gu6wXh0F4uoRRFgOzUpDE24yox369aNxiTCiLJuKloDsU6W4lM/HkE88xj+D9YpZZ84nnhYFMZ6vgOmx+7YsQMffyyuRk5MTMSnn36KLVu2RFywaKGnE4ivGEVULQq6MptCCTduiyJKH+gjTdHGtAUBcafHeqzMJgYLZgdUFCzLwmp1BwWtVmvDXHCn/iIDZD3J1IXryV96LE8EnK267L2BQokChBCcrohAWZswEvX0WH8PLBFXZgPGX0cRUFEMGDAAkydPxp49e7B37168/PLLSE5ODurk1dXVGDVqFC5cuOC1raCgAGPHjsXw4cMxY8YMcFwEUk2DQP5uNKovSIuiLlxPyg46UfhdFwvw8LdLcNVZ7bWNQok0x65W47Hth/Bbua2uRfGJrCh8PWenjq5G6aWfw/eBfhSFaFGIv9f7Eh6vvPIK2rZtizfeeAPz58/HddddhxkzZgQ88S+//IJHH31UkzeuZsqUKZg1axa2b98OQgjWrl0bsvDhQNYJmsKNvgwFpu5dT0SMeumu4ahk7eCJABvrDL9cFEoAKl3iPVnF1s2kLxjk6ZWvYPbhnxbghy3PhO8DA8QUlawnwdglPAIGs5s2bYqXX34ZFRUVaNmyZdAnXrt2LWbPno3MzEyvbYWFhXA4HOjXrx8AYOzYsViyZAnGjx8fgujhwW1RBHY9MSYPvRoBiyKw64nAQ1oFTrIyeIP5NymNA0526xj49jOS64kQdwUI0fVUD9dRyJw+fRovvPACqqqqsG7dOjz55JPIzs5G9+7d/R43b948n9tKSko0pQzatWunlDIIBXW5g1DJy8sDAJwraQ0gAaqeITh54iRsOoVhm1wuRazqdXVVhXKesGHrAnmdhu65KzsAaAkieG8/Wy2ufTl89AiuWINX6tdK2P8GtcRiscBm8+328LfNKIRbRpfLFZHvR++cJ22itfvriRNgLgR0VkQFTzkvl4oVoE/+9huaXDTrHaJ7XG1pV1iI9gAcDgfy8/J8lBsHLhReQr79KFpIr6urqgzzXAFBKIqsrCzMmDEDCxYsQEJCAh5//HHMmjULq1atqvWHCoKgCYgH23rUk6SkJMTGxgbe0YO8vDwMHDgQAFB5hMX+QpcmRtGja3dY+rXyOq7k8lZUFbpfN20Sh17SecLFsdwa1FQIAGEUGdX8eqAStisAwzAY4LH9xCknUP4Levfpg5tadgirXL5Q/y3rmoKCAsTHx+tuE1dm628zCpGQMSYmJuiYYrD4+s4rzpcCl06ge48eGNihdVg/szboyfnF3hNAdSm6duuOgZ2u8zrm91/En+G6p9nLNeCPnEFcTCwGDhwoWvu/HwcgPsOy2mjbPgFJvRj8vkd8HR8fH/axBQCcTmetJtgB1X55eTnuvPNO5fVjjz2G6uprC5YmJiZqShmUlpb6LWUQSXRdT75y5zxdOhFxPXnnXWs/U9zgFBgcu3pFs0l2OVHXkzHIzs5GWloa0tLSMH/+fABi+fHRo0dj2LBhWLRokdcxmZmZWL9e7KJ45coVpKenK//uvvtu9O/fP6rXEAqyxzbqBfdCwF8Jj4ikpHq4nnj12EIIiBSLFIPZxnU9BWUfOp1OZcZ/+fJlCLp1L4KnY8eOiI2NVUyrTZs2YciQIdd0ztoim4KaYLZPRaENXkcimB2olpPbdGWw8exJzTZFUVzj90O5dsLRj+K6667Dpk2bsGnTJmzYsAEdO3bE3Llz6+qSAhJJ//+eU2vx0+l1Xu9f2fUebKd2B30eWZm5dFoECBGc+MnPLaeuEAv3Y+5dwsNYiiKg6+nRRx/F//2//xdXrlzB22+/jS+//BLPPFO7rIAJEyYgIyMDt956KxYuXIiZM2eiuroat9xyC5544olanfNakXWCOkbhK+uJeA7Akawe67PWE1G2OnmtoILOzdhYuXKQRWmeqsw4D5jN4Skz3nagBdf1919mXN2PAoBXPwoASj+KlJQUpR9F27Ztdc/3+eefo0mTJhg9enRYriEScMpsPfznPvT7VpgYC+7oNk7zfuUv6yE4qxDf/a6gziM/I0fz3kdqt9c120gkFIXgx6IAUfQBX9+znh588EF06dIF3333HTiOw2uvvaZxRQVi586dyu/Lly9Xfu/duzfWrfOeIUQbJT02KNcTD0LcRhghEUiPVU7u421JBzBg4PJQXLwkD3U91T3h6Echw/M8PvjgAyxdujTygl8DgqA3GIbr3Ly7eYMKIvAhWfZEsSi8kwYEgfV675rxcD1xHj0n5Lynem9R/PWvf8V//vMf3HbbbdGQJ+ror6PQ/5JEi8Kieh1B15PPHcQfDAFcHhYFr5j+VFFc19+qmfXXRZlx4Nr6Ucj88MMP6NKli6YlsRHhI+h6EsDrT54ELqRYodLhTkeZRdL1FMii4DyLAhpMUQSMUVRVVcFuN/ay/GtBL5hNfJXwIDwAs+qlOFAfK7uAtaf3hkkg+aePBXeC7Hpi4BI8XU80mG0k8vLy8OSTT2Ly5MkYM2aMVxJHoH4UMt988w1GjhwZSVHDgrspUGQsCr1gMyFCSBM2JY6it42PgEUhowT61TEKlUUhaIPZpL65npo0aYKhQ4eiV69eaNq0qfL+Bx98EFHBokUoJTyIwIMQq+q1+MXmnD+IL88fxEPdBodNnoAd7gA4vCwKOZgdQZcYJSjC0Y9C5tChQ5gwYUKkRb5mImpREF5/8iTw4r+gz6P9qSYiMQo/rif1ZrF6rM4GgxBQUYwbNy7QLvUa3WC23/RY1Z+Md7t6uHANzgHuDzl7wgTGp+uJBrPrnnD1owDE3tmJiYmRFDcsyPdfJILZAuEBQasoCBGAUC0KuNNjPV1/AgmPojhRfhU9W7YWz+03mA1tjKK+BrNPnDiB+Ph4JCcnIyEhIVoyRRVBCQ6rS3j42Jl4xijEn5zAh29wDuB6UovprSio68kohKsfBSDWTasPyIMgF5FgNuftKJcURCiWgFIUEIz4AKvqt4XD9bT7YiEm7/kOswb+AWmdu3lbFB5KzZ31BBDO6b3BIPiMUXz++ed4/PHHsXz5ctx3333YvTv4XOX6hHzjBLOOggg8iCpGISsZjgjgiaDfBClE/Lmeyi4fBc+5lNesV3osVRSUuiOS/ah5wmtKXgCqZJKQgtnSsYxJ53zXblEU2cXFyAVl0mJYD0XBeqTUu4tBCyj89EnxBWMCQFBwnsOlMmM8yz4tipUrV2Lz5s1ISEjAwYMHsWjRItx1V3C5yvUJQgCL2XMdhR/XE9F3Pck/LZ5d8EIWyPemgoMfwOKcBLNUEcblUX1NXmhHF9xR6oJIrswmRBBTZNUoFkXowWwCRjxO9biGIz02VmpFoCSaeCoKj8+QXU+8esxhTAAh+GC7aGEsfqbuS8/4zXqS3U39+/dHWVlZVASKNoQAZlNwWU/iDanWrSYQVXwiLHEKP64nnteWD7ez2puOR8OxKAhPwDuMZX5T/BPpGIWXBSC/rk0wGyYQj5hEONJjY8yiolAWw3q6njwtCkUu9x+NYUwGi1D4URSe+d1m8zXOlA2KQACLKbisJxABRKMozADPuRVFGAZof64nImgzP2o4XiktDqgsigagKM6ud+HQaw03LbshEtGsJ4HzUhTXFqMweaXb+rMoBIHFicP/Bs+7fO4DALHSOOnwUhTiD+8YhWRReD6yBnuGg64F3JDan6oRCMAwgDnYooAq1xOBGUTglYE5PBaF74dMnPG4vwcGgI1z39zuWjvGuslqw9VD4sPvq385xXhEstYTT3ivGb+sIK7J9aRzPj2ulhzB0QNLcOXSQb/nN0nPp8unReHxmbJcqkeW8K6wxDvDic8YxfHjxzFgwADltcPhwIABA5SUsp9/DmO7wDqEEMBkYmBW60GftZ54WJp3BOTV/8QMQjjFkuDCUdLDn0XhlUvOwMayaBkjllpviFlPRJBiexTDI2c9RWTBHeHBeNY/E0J3PSnBbHgHs/25nuSMqEBxDPnZcyoxCmmDkvXkI5it+pNZmieAD7xyIar4lObrr7+Ophx1hkDEEjImzzd1d+Zhbqbu8xBB15NOUUAicBpFYSLaz2yI1WOJZ1hIfl/g8euhD9H9lscRE9s86nIFQ3Z2NrZu3QoASElJQWZmJnJzc/HGG2/A6XRixIgReOmllzTHZGZmYvDgwRg7diwA4MKFC5g6dSqqq6vRokULvPnmm+jYsWPUryUYlGB2hGo9MSaddRQIzfUkz9QFMF7H+VUU0rZAcQz5efQVo/DMepKfc9n11H7UPNh/24WK4vM+64LWBT7nah07dvT7r6EgKwqN68lXMJsIIFBXDfV0PYVhgPZjUYhZH+qvjNHkrDfE6rG+LqWy/DR+PbQcJYW50RUoSMJRZhwAFi9ejLS0NGzatMlnDwuj4I5RhP/cRCeYjVqkx/pzPflbRyEHvgMpJVlJyq4n4ul68rRilPRY8SdjtqKKb46zuDXAlUQXY9k3UcBeyAO5nUD6ETBmRnKliZlPCn5qPTEeWU8QOEVBhCNG4T+YzWliJIzHZ3IN1PWkh+wCiEghtzAQrjLjgiAojcJqamoQFxcX3QsJgUjGKMSV2dr3ahOjqO06iuAtCvEDfFkUvGeMQvIQKMVJTRasKHsa1YyxrORGpyhshQJwqRlYG0FMC0YJZqv1hO/0WAHE5B3MlmcJ4Z3J67ieCA9GrSiIp0XRABUFT+DTDQffDze35wq4H0vdb/A8HGHK3LPc2RaWP3i30VQTrjLjL774Ih555BGsXLkSLMviv//9b1iuIRK402PDqygEIoCA+FlwV5tgtsnbovATfwh0v8nIk0an7GLyyHrydj2JKFlPJjOqBWMpCSCErKeGgrwervzySZRdPgoiiK4niyaY7dui0OhWIiuKcGY9efxUIQgcQMzgpWg7Ax8xCoMriksVJ3G29FBQ+/rKD3BbFBGs+BkGTp48iaeffhqZmZm44YYbfPaK5wQBdp2vberUqZg7dy5++OEHzJkzBy+88ILhMmJk5DlLuC0KWUGIykL1R5LTY0NIIpGPJmBCWkchCByukDuQvfsuOFnf1+cOZkufpPpbEELAqrIUCXEvuBMky4Ix+567O4uPw1F0xOf2SNLoLApGSm/66esp4GIuQLgxV4xRBKMoBAEwe6yjENzB7HAM0IFcTwyxgjUJMAtmMABYVVxEqCfB7K+PLcPV6gt48d41Aff17Xry7wqw/OE6zazfZrMhLj66K1zz8vKQkZGB6dOnIy0tDfv27fNZZryw2gk7RzSz8atXr+L06dO45557AADDhw/H7NmzUVZWhjZt2kT1WoKBU7Kewnte9XcsEB4mKQ1OtghICJ0mlTIjfi0KfQu2Bp3g5K2wOQlirfqRZl5xPXlYFNLv2gV37nm64nryU9nh6g//BO+oRKfH/+1zn0jRaC0KWUe6XU9BBrOJVlFogtnhTI/VuVkFgQMDKzhGsigIowmgq2MUhBV899WoYzjeCdZjlblPfCiKYF0BdYVcZnzhwoVIS0sDoC0zzvM8cnJylF7xSkMd1VfWunVrxMbG4sCBAwBExRMfH29IJQGokimuQVNwAoHTY/WZ2uWkmRjI74diUahcT75iFIxOPrYgcMpiW97Px7ldTx7psQBACHhOfb+Kz7jZJFk4YDwmoh4ysA5t4cAo0vgsCukekH39yjoK1T6EC8715HTdB65aUAWzI5v1pFSrNQnKWg+160md9eR8+wRMNzVHzFjjZajxAgs+SJeRb9dTcMHFuiLUMuMWEwMn71nKgUF2djZee+01OBwOxMfH47333ov6tQRLOGIU/98PBfipuBx7Hvqj8p5GUah+r81kwb2OgvFyWSkWhc7iYra6BIKsKPw85urV6YQQrXnlZVGIo47FLJ6ThwWMyQIzOP11FAIHEsnmSn5odIpCqtmlKAqBiHrdFITrybMVKsvei/LjpeDM4av15Nf1JCsHyaIwQWtRqIPZ5KoL5GrdzD4CwQtcCIpC/7swuqIItcy4WRqcZryWhTZxMcr7ffv2xf/+97/ICBlmwlE99qficp3zet/j4ovQg9lEsSh00mNli0LHmudqykEgNm7zZzGpn0eXIIDRuJ4AVmVRyPEJixlwsmJyDGOywMzw4In30Ex4FgjBzRZOIup62rx5M0aOHIlhw4Zh1apVXtuzs7MxdOhQpKenIz09XXefcKO4nqROdaJFoVpHYYLfYDbx+AJ5lzsmEN6V2Xp+UinwyajcTXquJ0EQrSI/Qbe6JCSLIqDrydjB7GCRY2SR6OUQLSJV68kzRiGjxChqsY5CLAqo73rSSxYQeJfy7AdjUQCAnWOhmfERokmPlV1ZFmmWKjBmwGSGhdEfRwjPgggsbA4CIcr3ScQsiuLiYixatAjr169HTEwMHnnkEdxxxx3o0aOHsk9+fj7eeecd9O/fP1JieCEHs2Vnk0CINphtNYkNbHUQb0xtsIlwABcjWxRhdD3pfr74kzOJvzB8C7CC2iyXH1Qe4KIUoyACyvZ8jJYDHoYpNrhgMS+wXqUM/JxeF6OvowgV+Zti67Wi0P4MF4FiFLVdR+ErmK2XRUV4FwRpsa2/GIU6ocXBc4j3DGarPlNWPBZpSBEk15NJVUNI7QUjPAuWB15ba8cDf4jBbT3Vi38jS8QsitzcXAwePBitWrVC06ZNMXz4cGzbtk2zT35+PpYtW4bRo0dj7ty5cDoj7yphFNeT+Ef2WkdhYfz2oyAeulVgw5eWSjzMVO02AYwgCs9KXZbMQmuU1Lhn1LySfUUATvoXYcxV53H1h3/Cfu6noI/hQnI96b9vdNdTqMhfPWvwjDV/yKuSw2FRqJ8F3zGKa3M9efa3cN9LxLuyLM+6g9l+viL1ZJEVBK+sJ61FIcUopMFHgGxRuM+hdokTgYWLt6DGBdij7FWOmKIoKSnxubgIENMV+/TpgylTpmDDhg2orKzE0qVLIyWOgpLQIAezBbmEh7TdavI9Exd4gGgtCoEj7mD2tbqeNB/rUddG4BWZZdcTA6BGZf3w0gl4QRCVnQ/LyBcnLu3B+rys0GQWOFRZCARXTfCHEP+KQj1IBHY9eRZZq58zclnqcLqeov23COeCO81CUkFfUSgKggheA7tvGaVD9fpR8Oo1Dt7WhhzM9vcdqZUkywvarD1CPLwOcjBbcj2pYhQyGkXBc+CIO64RTSLmehIEwefiIgCIj4/H8uXLlddPP/00pk+f7lUkzR+eq1iDojwWQFelFEdlVRVMDOByiAOdU2AhVLM4lpfndWhLnkN1tVaVV1ytAttcvOFOnjqF6y5dg89cAIDe4u9ETIdUNglOxQqSXU8AcLbwIvIcYt+GaptY1rb08mWAdEZ1RTWO6lyHL365shEnK3egM0YEfYyDu4zNNwD3nNqN1s7EoI6x11SDQMCBA/t1UxELSlqjD8SmWSePnwQqbF77VJWeAQAUF19U/k4mkwlFRUVo2bKlbll8m837PEaB49yDbDjkJISgoqICDodDcx+FC71zlleIvRqOFl/CgQNV19SaYP/PPyNWGiUrXReV9w8f+QXNrZcAANaSE5CdnT8f2A+YvIczTznlMt8EJvz22wkUXW6mbLtSckl13AGYTO6kgsqyUqXO2/ETp2AvrtaV++JV92T48NGjaFFRDnmd9aGDh1BeUQ4w8ZIMorxOhw1AEwiMBUeOHgP4rso5COGVa2jhsIOVxoAL588hz16hK0MkiJiiSExMVPK/Ae3iIgAoKipCbm4uxo0bB0C8sS2W0MRJSkpCbGxsSMfUFAs4trNGyXpqGh+PGIsJTavEBzU2Pg4QCAYOTPI69vQuID6+BSrda6bQrGkLCNJ88IbON2Jg54EhyaNG4AgObpSa9fAckjo2RdCVhTIAACAASURBVGxiHwAA66rC9jxxGiFbFCbCoMV1bTFwYF9R9h0/ANVAm5Zinn2zuKYYOLBP0J9//uBXIJUC+vVPhlnnodNjx5avAQaIbx2HgQODu/btl0wAByT3vxVWs3ftovyvaiBPxbp364FWfbxl+S2/AGWFwHXXtcYA6XNZlsWFCxdw/vx5r/1dLpdSd8mIlDlYOHkBDAPwTUO7p30RFxeHpKQkWK3h9WXn5eXpftfxVflATSUusi70vHUwWob4bAIATolFHm/pm4yWsaLclyp+w/ZCcfPNN/dG+xbdAADVBVdQLC1U7t+vL0zWJoHlPLMHIAQEDLp17YKOXd3b82xfwHZVPl8yLNamyrYDRU1BbOJ92KVrd/Trqv98fHNwH1AldgPt0esmtDxcCOGieNJ+ycnYXnEKkIxvIjl0WraIR6ldAIEZycn9se23KkDJ1DUr13BmN0BY8Zju3bpgYI/Qh2+n01mrCXbEFMUf//hHvPfee7h69SqaNGmCr776Cq+99pqyPS4uDgsWLMAdd9yBTp06YdWqVbj33nsjJY6CO+tJSo8VCEyMah2FhQFq/ASzvVxPUBTFtbqenI5yAOJgxrDVuPzNW8oqTLF8h7dF4VC7npRot+SHDTFGwUmmNy+wMJsscJWewuVv5qPDA+96PYTKZwriLJLjfLueSne+g5jruqBF8ljl/OJPDlYdEzpGbYl6/EkrnOKAKui4nqxWK7p27Qo98vLykJyc7FPGuiZj11HsL65GnNmEbx/oV9fi1Aq3x5Zx92OoJdoaZv7XUYgbgvs8+bT+YxTQ2cYGuY4iQIyCuBNiZIvCrIpRMCazGKuQz6duaCRw4BltADxaRCxGkZCQgJdeeglPPPEE7r//fowaNQp9+/bFhAkTcOTIEbRp0wZz587FxIkTkZqaCkIInnrqqUiJo+AVzBbENZFy1hNjNQEcgfOKgPNbnNoOa0QA8VIU7kEt2GA2IQRXvs+G68pZzfsVV06qdgIEl7sVKBHcBQFZVYxCvYqVF4vHQHG9hhijUAZ96aejKB+O3w+Aq7zk8xiByMf4jq7ZTuyE/cxe1eewmp+eqGcvnn/SR7YdRHpOXoMLZrPSKOtqAMFsAJpsvGBRx1RYHzEKXpM1pBPYDoA2Pdaz1pO/GAWnuJ54P2ldagXn0lMUqu3yWKLEKBgzYLaAV40xAlEF4HkWvDSRbDAxCkAsozx69GjNe+q4xPDhwzF8+PBIiuCF2yUu/qVj7QSDfhZQ2UH6Ai0MCE9w8hMHnKUE7e6wIu46sRw5iADCW8GY3TNdgXcrimDTYwVHBcr3roAptjliruuivM+6VL5pwoCwDvcxAqcoCiU9FlqLQiACelSlQSi6GcAp3ewtgQjYf2YDBnYeDYtZ64rhpAeFk/oCE6nMhloOr/NJSsVfSQ7COUE49zl4aXDnfQzyaqk8EwvKndKxQXYcqy/I2U4CEQcbi6n2/v26wh3EZrzKcASD+hhtDTPVIjW9BXcIbi0FIcSjKKBWRvU59NZYuIPZ7vftToL8cxxuv0lSIhqLgvfIZCQeFabFMaiCrQEQI6bHMhbwHvN3gUglhgQOvEVeexHwcsNK46v1JJWJlQfdOCcBQ4BYeXGa1QTwBM5SWYtLByrdtKwwq1yvAuf+EwZyPV39hcOpzxwgnDQQe9Rt4VzqABmjGVyJqnyIUusJgFM1Q+GJgARnMszyezqupwtXj2LDz/NwoniP1zZZQcgWhSynwPlRFESrXPQgnBOC6lplBcH5tChUricf402pSy4M10AsCvVMtBaDrBHQZPzUwqJwqK7bt+tJNZirPyOIz9M4B/ysowDgpUQ0tZ5Umw6e5rDqexfKbXLmo/pvoGdRqM8rni+/TAx6CjCDMVsgEO2wzAuA3JzJbVFEdyLR+BSFUutJCvBJ35tJ+j4ZSVHICC45n46XflhhjnN/SaFYFNXneFQc5xUF4akoWIdKURBGs12QKscCqvRYwmgGFXk2Y5FNVR1FYXeJJRJcOjEF2X3EyZaED4Wmhida5aKHwLuUcwhEUB52X64nTd0tH8//JZccY2ooikJ/Nl2fUB4b4u7wFgo1nPsYjUs1UHos3EpD4JzKfesJUeWfE52V2ep7ySvtWuCUBXcqMeGSdnNKtzInCLBIgwwr8NqUd6JVpvI6CnmC+UtLEy5VnQHvoShqSk4pNZ54RpShwcQojIpnMNskfenKLNxjwZ08/sk3FeEtGkVB+OAtCoEV4wc8qx2IZTSuJwCCyuWjWUehCmY7ea1FAQBm+WbUiVHYXWJKnYvXURTSzSjP9BWFxvpWFLLryZdFQQQe4Fm3olA9gL4UhaAqPeJpUbSOFf8GJa4YaXtDURTua66N28YI8CrXU21iLeo1QS7Vs6Ee0H3FKOQZd8mXs1D85Swf8rl/F4PZnuso3Pe5f4tCZTVIkzEX515sGCeN4noWBaejKATJQ3CmmQXHL+VCgBlNhHJ0aCXuW336J7eikJQVdT1FGKUooPSlM9KXroy9VpM4E5fLYSgWheR64i1QZ3QStetJdRNvzXNhzhp3MBoAHE5p1uOQBmIPv75WUTCAwGnq2TA6C+5ckvyEEMVvb3F3kNGc3/nhaTT7UbqJddxJvMegT/jAFoVsHfhyIynnkJQNT4JQFCpLyFP3No8RH5QSVlQUDSlGwSi/e1uCx65eQUUUKhdcC+qFdrJFIRCCXy9wQS3+01gUTrd17SvrSS9GwVVcBFdZFFA+vVpPvOo+93RLEcLrup5YaTfZouAFAU2kUuEuXqsoCCEai+KGFrdIsshNjsxgeQd4YkIf7hsM7ipNrjhWURQVVilTirqeIoxHmXGTl+tJWzvSbVFIwUYvi0KVyqaahWw7yOJqtfbhOF9eJe7n1A7A+06vx+6Tq7QxCmkFpttlwynKTQlmE0bJlrGf3g3OKVoJbouCaB5Q/kQV4gqlzCneW1EowWxBK59ejOKnEyx2HHaBl2MUxIeiUNxs4jnUysG3RaE63mNiKrv3rnCyomg4FkWcdG/qxSie3/0NVv9WEGWpQsM902bgkgbavcc5vL/NibxTgV1R6hiFw+meNAUTo1BcT2yNxhJXI2hm897VY3neMyYIzWslmK1xPWktCo4IiJMCzqLryTM91v06oam4HoQoJTvcioIRXDBJzxTLupTilz+1kWIUUR65G12ZcYZhxC9GUhSMFGOQFYX8DTAQF+UoMQrZ9cRZYI5VqRJBbVF4P+C8QGCWq0NKLhXeKbt2xAH54O9b4eRsuM3VRZXxo1IUMU3FGZMco1C1QnVJH8nXlIOBaOooioJAjMHIuswlAE7xAN0YBS/HKOQgtqwwvB+8n0/zKK8W0JVwAOPHolAC4pJFodrP9zGq3z1m17Jbxik0sGA2LyDOJC7h8XTbcIIAO8ehkvUdBzICGotCGoTLpMlSaWVgV5RdNQK7VCVheE1RQB8BbNny5hzQa/olyuf+ncDktVaC55wwW+LAcw6d+AXvTo/VsSjkWAVPiGJR6AazVedkpFiEYlHAAhfnhAAzzMQFRnAAaC42O1I65jWwdRSGxkTcFgWRXU9y1pOcFSXlW0tjGRHcFoVqZT8I79a1coxCkw+uGscYac2FSyrkJw+eLF8DF1cDTnY9MQLkm5131eDbC1fA8hwYYkYcW4N4SeEwcGeHmKxNYCJiOQKz6ok4cop1z/RcAhhprHEFY1F4uI3UuFgCFwfwkAPTPLYeWYLVP03X7OcZuNdaFPqDPPHjepJn2075IWsoikIgaCJNKFweLkOHNEg4uMCz8rpEE6OQvid5QAtGdHWqt8OpTg33v+Cu0kLw1t7/hyvVF0BYh+7EZsimNXj3iLuch17jIp5zwGKVymt4KGtCBN1aT/J12bbmStvUFoXegjt1/E1aeCfXdiJmJc3cRDgwrE2Sy+16gqSszFFOn26cioIRFDeOSerxoJihVrdFAahiFIpFYYYpxv0lMTDBJM1u5RhFZY1qZqUa9GTrxe7QBoudnB0uvgasPItiBMhfTUFZFabnHsehMhYMsaJr+Tm8eEy8WcRWqFDkN8uKQnUzrtrpwPFCHoQTC5QxLlEGfdeTZ3qsb9eTiwOcHHHHKMChsKwAhWVa94igBMQdoo82iGC22ou1r0jbyEZOB3YpiqLhxCgU15PHIFUjNbupqaOmNcGi1m+yRSG7SIKJz6tjFGqLV+1u0otRlMUAdrYKpdXnRNeTToFKJ88j5+xp96EweVmjPO9UynbouZ70WqEqwewyMR7JE4I4s79gtuqkkqJwWxRmODnxfjZBpShYUVEIIACpmwV3jVNRmATFjSPHKMpqxPiAe52F+L6S9SmtyiaCGR7r1GCVg8ySQ720Uq0oVB8rWRROyf0ju2VcnEO0KFi3RUGkGEWlQxxoK10CvD2FRFEUgqsGFklRWFQ3p4UQsSSx5KMys9KsVcf1xEsWBK8EsX0Hs10cgYt1B6c5wsHF2eHitAF8d2YXAXg2qBgFVG8XXHHHbQghykzVJT9kDcCi4AUCngBxikWhHVUd0sjk4Ix9rerZshzMlgsDckE0qVC7npysOutJvfjOO+tJuqXhZO2KRaEtU+62dJRjGa1FQQjRWBSe/bQFVYsBXdcTzCA8D15Kj7WaTGKMQuvvUio8S0JIP6TxAGalA54JHOASY5ocx4MILDgGivuLKopowAiqYLYcoxC/rOL9ygo78BaixChEU1SMAagtCgCIEeQbSFYU6jQ/934mKZXW4ZKtE6e0jx0sVwNWCuARhgdhLDgZ1xJ2lzhq2lgejKBVFFYTcTeLYWtgFcTZkNr1ZBYInCwBkRUFpw1mCwKLrWuG4/ypbYrriVVcT05cMTfDPrv3Q+5kxYdEvmae8HBydji9FIVqLQjn1DQs0lMUpY5KXJKKqnEMgVkVA9IsSpPKHDSEGIW8biKQReEwuEWhHpDla5CTLYKpJmNXZXW5VIqCD5D1JNXJg9NVCUCsoABVyXC9v5u4jsItFBE4EML7sSgEd+MitaKQTu0yWQAnC44QmE2yovC4aELggAkm2UIicnlxcb+TwqM4WS1WTTYTDgwrTpJ4TuyVLSZY0vTYqEFUisIs3dvyTznxiAMPYiUqi4IHIbKi0J7PKsgWhXhzqZuKqF1PZklRVNbUSKd0KwqecKiS6yWZa8CQJljRLgk2lyhADU9gFrQfbGEE5abl2BpYhCbStag+kxAxdU+yYiyseKPJioJ1VsFhv4yq8lNKMFttUcy6YTxetbfx6jEgXxcrRcp5IoguNM6u7Sehzk3nHAEtikJ7GSy8GSwDCAyDOMFd+VS9vsCFhmNRyD5vOZmO9Uxrlga6GqNbFIK3RSFPlBxBtOW1qRWFah2Fr2J9xFNRON1ltwXWbTHr/d3EYLZq0iI9D4qi8EyPhduiUMcolBpdjBV8jR2l1RfgdFXAajKL7jcP11MNTLBAuk45IUOyKAQ0wVWX6BUwgQORLAqBFxUFy0DyhHAw0RhFFDC53Tie6bHySmvOJIBYiBKjIAIPQsSB2NOiUBSFNINwuFQ3kuoetUg3xtHKU+I5WSc4gVXcNyeaSjeORdRWHJqjRjqBjRNg4rUluWNMYmaWkxfAsTWwEllRuPcRFQVRXE9WPgYgbteTq0ZagOeqVuRgVTGKMot449o57aAuDwAc3JlYLs4OAqKJf6gXFRLOqX04ifcDXM06EEPMYE08BBA0EWKUAUh2ycSZTXApjaeMPXgGg0uxKCTXpKdFIQeza7HaOZqoq8fKJTzkCUVNEEtAqp1OMEQ+Tj8uobUopPtVdj1JAyugzdSzK+dSuZ48gtm8tL/VIgWzPetAqYLZ6q/BKSlAl8mCKlsJnJwTDrZMtCjkdRTyoE4IHIwZMZKiINBaFAwYuAQ5yYYDIy2O5TgOEDiVRcF6ucYiTeNUFAyvsigY6ae4iUg+VZbhwVmINkYBaSCOAc513Yizbb4E4G1R1KhmT4dKLuN0pfiFWySLwikt0hN4lyZWIK/gECziDU9Ic+Umt3MEFkHbVyBGCqTYWA4c64BFaAICQROjMBMCJ6dyPREzzMSiDOb2zz4XZXJVKsfwOgvtKl0qV4BAFEumjL0X/397Zx5mRXUm/F8td+8daEDEBZdodNTEuOCG+E1ABZQYkxCjYJiMyQzGCU6SSSJjvujEOOgzRqKTJ4/jmBjRzyVRYr5RSOLnBgQBFVCRTZqlu+n1dt+11nO+P6pu9b3dTaPGhjbU76++t+6teu/pqvOedz1SjMZFBm6n8t9U0a/KMStSYgezKPKOSUSoWKrAVSQxNxoEOUsTal1Mx0b3q2s//sHs0go14T+NtvvxdD2VxyhKys73nFIw34dFYZkkpOd+tdz9KYqBDQIDi8IuK9Irq6UoOgPvES+YXa4ovHt9fxaFQATxgZIbbVemyDY/iG2pEYqFdBAkj6gqtiwpCrUkMAYaEenJVvpZUilTFK53jfdiE8D0665c4e1up4CUEVAcTLvSxTvcHKaKoq+OQgssilJgyU9LVQSOWh6jcEH2xSheb7iPPXX/D4CoKN1AftCxLN39ie3b+fXWt5GuRKOU+++vhh2zX/DXVxSaN2nrooacXa4oyroRSkm9K5iUVcjZrqcoZAJTzVTEKHRR6XoCiDqxYDK3ezoBz6Io0b8pIFQqCrPsueu05+JYM7EUEUz85XGK8nMI2+xXR7Efi0Ko2IqnKCJSCYKcJddTjV+dbRP5q3A9lWIvfQV3/dNj/QXICHc9lccobLefRWEdWFE0Nrv8x9oaIgJs1+u8+nrnTlx3cNfTwBhFWeJDmesp0zuwTX65RWG6gre6vEm5FMw2XTd4z7uu7Kuj8DXFnlwxqIWwVJ1iscePfVi+ReFWKAopBIaiEcFTFEEiSqkuSirYvnv5DzUXIf2+bK7r9otRWJjO4DvsDReHpaKQZRZFyeUUWBT+kDiKwNZEX5WwFEGMQmgWjjBxVW8VEpE6UVUPKrMNSwYb8hi2JG2a/aqNo0ikryjKLAr/ppOqd4Mk3TgZP62i6ILq9sUoVCmYsltlyboIOcOmq7CXiEhgqdkBMYqavTmsR3cH76WUeizXQAqB43gruJ70luB4UHBX1r8pWxZctPo1G5SiDkOVZccHVxTSMbFcC8cvKxzKorBViauAJtVAUZQm0JpoaV+O6F+F68nu53rqH8w2PoYWhRW4nrzX78eiOKIdTsjGGGV6Y/JGVxM3rPwvdhb6JkW3oo6in6Jwyqq5yyyKfWse8f+qdD2VlM7zTR3cvDZNXqkKLIoX2wU3/GkT3f6qT5RNlY6/YMlYDqr/vq1EKJq9fuzDJqpq2EIghODfj8uxLeWSsx0k0SCYbZTa75S15Ol2J/rySSwjH1zv9V7B76uuBBlFwcawD+62voelokBxUYiAopa5nkqbh/RZFJYqyno9uUi8m8hSvBvX8l1EKSdBTNP7YhS2pCZZSrPV6LVMTKvvBldEDFfxJtHBXE+W1hucN+PflAWXCteTJl3qTYWEq2C1Gxh2Hl0ksNXMAEUx5YUmZHufG2lUZLzneiqa2KqfVZVvD46XLAq33KKoUBT9xlPWUtbyqjIHvsL1ZLC8tZc1ynwkgyuKnG0QEUrgetJQ6DUqLYpavzGgQ9Rr1naAPkKyaDD6raYBRVTlCEfS+pKFM0iG13Az0KLonx7bF6Pon1QwUpBSlmWCKpj9LIqCyQH/TzH/Vqm3XGwhaPPjZ91l956QLh1Fi/+ztQXhVsYoKhcoffddvtjnVg2Ol21c1FY0EUBOqQ0sik7T6zXbUSwpirLCWv//02s6gSfCUnWMQFGYRFQVSwg6NYdnGw1WjrJ5aG8a1T2SXuntYFjq01aKUWhS4eL28WjCC56X3M7CcXmxR+OV5P/yAuqKjRkqiuHHa+GhkUyNLct68l1PwapDYKiisteTb1FYeAqiGPH6yNdb1cQUFcuvrDYsAkWhSo0e06RYLO9RU0VrJE5rzOG+F64tE8yPYah9iiJXMt1dBbUsPVaVglRpnt1rENuyiONyKRw1j1ZmnuuDNJir08ZiOUVkoYij+cV/ZfH5kqLIiL43s5aFlJL8jlcw+2kKKWsqXle4ntxKi6KlYGEpKRziFYpCdJiIvQXPopAKlioRirdXSG+xVDvhK4oyiwLkgFTG/rivb2bshh3IPfvfqa/tFZuWFTadaw9+zKOkGKIKaMog6bFllkR5++6vvbic3zVtPzhCHoD+ZRIlK6l0qwhZ6bIcjLjp3W91lsQSkrT/PGXLYgxCuPxozTbufbOJZjcOihpYFBWKwi5iCoc/t2+nYJUWSaVn0kWU7UfR4wtWVKoDi6LXjzMGG2WVb0/qW7jlFkVzfAxtOd13aZlBHUWX7n22OyrZ7XdkcKhGcSaw218AlSyKiYUYl7aP5oii9zwW1ZLlLUnbgKIiZBKwMULX00FA9TqxxhJjAwWhSgVBX4xCF2CoblmvJ4H0C9oMxVuhWFoGR7Gos6qJGxkyac+9Y1iSmoR/XjR6LCPoHAtQYAy3HnU++yqTmAKLoqDmEEhSThx/PxSKrlJpUQhB0p+D9T0WmjmB8UYMjSxqWUcZbZBVXK3WiO0ayHwRRyutmPoouZ56y1ZRGcvE3PcO+37zLXqb3qw4n5S1Fa8tt/yB7bNkWgoWad+yskhVKArjlrcwbtscWBS26ll1KmWKYkCM4v21Gi8pCNk7+MMlpaRzXWn1NuSphoVSMFtTFG8l2j9GURabKCmNnG2zqbuT9R1tB0/QIai0dJQB6bFQ2bFgMFJ+14A6W+IISdqvK8qVKQop3WBi7ygYKHq0T1GUZdsJ2+BX6fXcuPohOvtNcyouEgWj0IHjFIPzFZSqwKLo9S9ZOlZhUfiKotfsUxSWFmVF6xwkGq5rEtE811Na8wYgHZF0WyWlk0QhSrcfuC5ZFNWOp4ySLggkRTXmH9foKZVeEPcsCicMZg87XowiQjw1Nghma8GSus8VldUdnILnlpDCRYixKJqgqHUHH81Gu6i3ahhjF+jyv1u0IRlTUFWJJjUKjkOu2HezR0WEghYlP6C60vt+h3suRc0g5SQo+im1RaGgueUWhUvSf7AS7d7EnnQgSga1bEUaHcSiqFFGAWDnMzhq6UHowxE2Ujj0qn3B86xtYfuKsNDTUXlC0d+iKMt68usoWiJjmbuliraCd06zTFGUuyRylokeWBQCVULW8Ps79XM92Yr/IB1gdhclRdGTHfS4WwSrx68LyRx8107JgtAViGrqwBhFmRVRCmh3FL2JoiV/cFeW+6MUnyhtDmQFFoWk1reue/IHUhTevV5nSWxJmUVRnlLtEvVjOc3pLhQ9hlVyPZWnZdsG7xieO7VLT1Zcx1MUKh2ta9m07mfs6NoFlBRFyaLwPtttVO4DAeA4gmU72ljdWtlexvv9iud6UryCu7TujUM6KuixKi1f0z+nV6Mhqba9CSHheONY8C0KgUbaKaXSxoHQ9XSQcACdFS3fKHM9qV5Zv3/TxRWFjogFEn7+zH08t3UFjjwCkRAYdp/PMxvpod6q5qh8F71ahIKRxbAEu3t2oqoS1Q+ad+X6buJSJbehpDgyfUnwvlJq2yHOp6jZJN14YDYXhYIq+jSLJgUJ/wlJdXs3c9KBGGm0MldMQ3kKlk9SVnuyt5jY4lign+vJNZGOTY+WCt7LWBZOr9fnP5OuXMUOsCjsAi3Ld7DnB48QfeY0kApN0SO979peirFFKsh6kp19MipZiS7AVCWu6qJKyBUrJ5/A9cSBW41Ly0a2eZldsndwRWF2lxXylSkK992dyMz7n4i7DYuVLd3v+/MlShaFrkBUVfabHgt9SqO9pCgKB3fC2B99FoVEqYhRQGNtSVEMjBG1/X4R7c/9CFdIauySRaHgSAKLIu+6qIqOgoKQLqpv9XfoozzXk/9YWGXFncIuUvSbhvXoCaa1XsLfZD8BgIYTuJjfbl9FZ9G7TkGpxlbj/Kzm67T5p+oxbba3vUabPio4t+MK/uPNnXQUBvGlSR1XGEFldrdvUXRHJb2upLx/rON3ekDxlEW122dRSCR51XtWBBo9/iJRyhgK9v7b3wwTh6WikIoLMkqXMbpSUdAXo4ih0K57d0u29Rpebz6DXv6Gdkuha2ff6iIdzVBvVdGu/A2qcyJ729/DchT+nMkhsFH9VhPpfN9NXKq7mNT6YyY3/Yia4jGA12CwhKG5pJwEluJNhobw6h+Cc7g2mlQwVUkq64CUJF1I0F7hbhoziKKIuN5KfMer4ziy5e+48PXFNKZPCY47wka6Js3RUShSEJc9dBV7sXuavfHo7ux3xiRS6lhEMIjhtCRpfXkckcxZaPlqdCbQGvFaEzhuAiTYVAU3u9jVZ0bHexQiEixFIlQHBcj7GTOmU3I9lTZw8h40OcRDI1vag347+7MorLR3PFLtYPX6fXcME/uBp3D++Of9nrs/j21p4duvvktX8YO1A0/77o24WrIo+qfHlimKkkVheGPWaRQ/VDZUcz47oIjyL6GvWrlUHOkrClsypta7r/tbFFJKCjtXUdj5Z4qOS40/bLU2OFIJLIqc66IqGqqiIYQTjFeHPgpRSOP4Tfgs0feM9ZZtApZT6pnRWsPf7vMWK5p0g+zGXKEdC8+lnFWqeM+AvFpF1l/B12+MsubJ19kam+CfzcY0DSxXBIHsjLqXs9LvABCVVZhE2JjehenYpH2ZunUFA4WgfTPgEvNHzLPDqv2MkKQLCi551Y+JKjHMIEYSY6yIc9rE6UP/Qz5ihlVRPPvss1x++eVMmzaNpUuXDji+efNmrrrqKqZPn84tt9ziVSAeDBQnSHXV+wez/dVKREKL5lkBVXacvH00EVHFMZkOzvnN2GB13xUtUGtXsyX6KVT3aJo6WwGv/bYhbVT/H9xbqFQUKStBQ+FMAOoLJ5UE82WAogYNZi2O2hfIUIWOrShkdUj5D8I7oYOFEQAAF+FJREFUtQJNQtS1STpQI7ah4mL599X4wsDOr+Nik6hLHEHMSlJr9JK06jmq4xwEqte63DVxrAI7YuPQlQI6Bh25Lh7MRWmKjiE/2Eb2spa1+nm8GrkEt8178GL+Ci8ijmevryi8ivgojlpDfu/rGPs246zqUzzVPZ5iNDWBrdioEgpWP4si5uez+1XjwrG97riAtMv697g27u4WetV6tuuXYHYPbIQIYPaUlMMLWGm/Y+7eNgQOYu/+A+D92Zz2rI+NZfn3W9I5frahachspS3pPHFNpTHixShybVsp7lobHK9wPflKoWRRAOz7gFaF6bpc96fn+PnbGwAvk2jZjlZW7O44wDdh7c5n2J1bM+D9Pt3m/fFOuotlO3dgOZCKClKxgYrCzXciir24uXYy6W4SfvJEjaXgoAQWRUcxDUhUVcMVLp1FE0VCa7SBzpjEppQ+bYCigqKysSzTqcb8BBEJjYbXlUDFpbSfYEZYOL6i2BepYnve9BYyUgUJR+5Icmz7FaQZ7f1ObD+JgiA+UVAtTu/1FYVIkFdSpK0cOdMgHfV+cy5SSm0vy+AKdp/xVEWV0+d6SkiDvO/6zat9lr2UUaoUjWS00t073Aybomhra+Oee+7h0Ucf5ZlnnuHxxx9n+/bKDI3vfOc73HrrrSxfvhwpJU888cRwiVOBVBwgjiK9AbCD7o0E/s5stMg+1UAqkioHcI8k5ug0FnPEbIWjeo5HyCgFdRS61Em4o1GI8k6X3xpYVmPjBlZAb8G7QQzVIebWck7n3wbyBIqiVEcBuNLk+EyCKW0TGGdaqBIiTh2WBtuqIenHAd6s9yaOqOutOUzGoguB5ftxxxUtumN9FlAxViC6R+ErJ/2MhGOgAK6iUJv9JC8rN7GHs+kptPO//+/zpMxvkrDHkVMSvJsXPKNN5LcN59ASKyv88zHkGIqK59/NtNWgqh1E/cZsEWsCu5NHkqTZ/50JbBHD6HqPjideRryVoeM8T5E0ZH1FoQpsTBpNqGlXcF7tROS9CaHkenJ901w+0oXxk3dZubaZwj+9Sc+qbtZtt9j763l0bryfXuUMJvUo7GsfD4D1+iZefeO/eOuthxGujdntIrUCzzZ8AmFFeW57O2bTTl488Vn+rC7FyB/YnSSk5N20979/s6Nvkrr1z1t5dEsLK9b8Cdt1ePy91Ty6Y2VF5fG76Rwn1qdQFYUYNh09Xez73fdwct7EXXQcX4X3WRfliqIln0NKyaZdDjvbDtza4Y3OdvKOzSute5FS8tT6u7hr/bv8x/ptmEVB5zq7Yt/yErZr8OyGu3mz6/EBe4m4stKiAIV/f/01hITe1x4g7rSRzlZ+x+romw+yZbv31dgqjlQDRWER9TLxbIv2zS9QYzTwqeypOOLTvJnyFluaAAuBVCMoepwXjBxVapSTIzHGF48CoNZWqHai6DiBRZEpc6/aVLEh3Y43KyiMtqIkLJ2YW0vC8ly0jupiK0kUqaBKleOy8E/bj+GBYzxZYyKJocQBScbJ0RUtFdN5z0yNU0WNU+Vf0d/SQNr+POO9TriQUgzyqvc8FcsUBUTQ9P0vOoYLRb6fzWw/BE8//TRr167ljjvuAOD+++9HSsmNN94IQHNzM/PmzeOPf/wjAOvWrWPJkiU8/PDDBzy3aZq89dZbnHrqqcQGmbSGIt3Txfp7mhhfGM+OKoWTsmCognGFLHG7yNpxk5jcup1XxsJroyLMam3AVjTa43ByxmVczvPPrxkt2TRKodY6gnO6oDlusjtpoktJtVtLWs+QEp4yymm9HGEkOLZQhYtXGwDgyByjjAL7UlW8VddGyknwycw43qkRTGlpIyIgnagnbmd4syHGp7oFOd1k1ZgIs/d4K9+Hjk/y1e0FemI15GJVrK/fwzntMeIu1PlZFm/V13Fq2gu8vT7a5bQulbXj43yyK0a1maErOZoxhU6ePkrHUhViEpLuGABs1SCndVNqyw6SuIiT6BfAzuttWKoDEs7oacRRspzS7Vkze1OClWOq0Sjg+m1QVAyi2FzYGgcUnjjuWP5u8xZ2VenElUbW1Gc5rTdGQkSpsvLUGb3sqFFZ15CkRu4jo4wjQoGUW2TanijlG9h2xXTWj2kEIGX3MLmtgAKYmsqqcVWMK8bI6XnqrRSO6lLtxElHLJoTKqdmdDbU5DD0NBqOP2UoKGXpkd76z/WtxVJatUqBav8vQYIcIMlTi+dUtNEpYPlVuhEh0HDR0ChQTQyDOBkMUhhUk5Bp6u0kDUYN71Zncfx2LXFsNFxMIggUvJ1VBEmRQBO1SFwcvTUIKnvX0jg6N4q2ZIasbmCjY/lZPEkMLMDBizMdV1AZZ1SxK9lLdzxTuS0wEtdfvetowZirAibmx9AdgX3xIt6WihYqUOuMxtT2oslqVBHH0faBovhjKIJ2/tWWzsWtGo4iyesqf5gQ8Qpa/eNRWVqJqySdiRVu2ry+i1I6Rkw0AApZLUtUCkByVP4Ixpre7327ukgmkuXoQi0aJruSPWS1enZWaaC6gLfDnEKCowoxzuv2xmVtfZpCpJ5uvY0GZyw9ei8OLtPaG6h24PW6vWSiE8hqPeR0X9lIHZWiV5slo6TcRmrdagSC9kgXcRGjzq2hKbGJI4snM6VDQ5WgovDEkZsxVJWjjU9gqB2kdc8xPtpqIFX3HjfO+zQfhg87dw6bovjFL35BoVBg4cKFADz55JNs3LiR22+/HYA33niDxYsX89hjjwGwa9cubrjhBpYvX37Ac5d+7IfhtTU7mPfmcYOfV4vw8InHMm/LVqL7qc1yFGhNRpiY/2j8u4amER+k2VteU4lIlahwcBQN3Q9Qb6tJ8dL4MXxtSxMAW0cdz9E9u4m5lX7xXCSFpUVoMHrYVTuRI7Kt6MKhqe5oju3ZFXxudyqBERvLid1NH8nvKUcARiRB0h7c5VOiufoIMvEajkk3kRhkkyQAQ4sRdwfvLOcqKq6iERU2hh4j3m//jGwkznu1ozi5u53oX0FvqL9mcpEUVQc5o0cCOxomYfffaAYoaOAqUD2EV7w5XmSCkfhIZGlOwIShHxdeG93G2Rel/6LrfFBFMWx7Zgshgk1LwAtclb8+0PH3w4exKM4880x+tfRR4spEIhGJK22iySwRI0FzokBt7Vbe+IygtydGrxtHyii1lhdcczSN4pgsalzwdu9oDCFJaQVEwSXl1GBLQcY0qY26jEqq7MpaVCtJJIJeIXDiJqO0BFFNIStUHM0hHVcY1R5DSElEi+NqFpFIM7uLvdRVTyJpQkHLkczGKOoxnMYCp1TnWT8pS9EqkEt0sc6OoTs5jk40oIgI+WKGZqUTR5ckZJGi0o4qVBQJmp5lk6ERcRREVKVjVA+4bbzdDaOII4VLznWoi5loREjo9bhC0lbsojqaIC81UBTiUUnOyJDPZ6gbMwnVgpjtdcItKJJCSmBHNaRtksq5JDRJjZpCwyFnGZhqDBdw1U6yySYkCm+bcRKGRkSBZE2alKbTYWh0CJViRKcuFyUhBTFNxxbgKjHQYuRkN0KVNNiC7rigytSp1QBVp8dxaG0skC28zEb3eGrzFmZSEHfqQFcoWL3E7RRutUZdLI1TsKk260m7EYhGEY6N65oVK3RQ0JQorqxUSBqCmCJwUTClZ23omk5c83Y2Mx1I+G1erFKrGBwUJAnFpVgokEgmcVTPbnCkARGDlOXFqRwJTtkaP654DhRDegkammbhuhpSVj4TEokRM4haUTQ/uSKuSBxNw3EkKgopzfP/57BxYhY1ZhJjkDWkjk6hWCSaKGtQqaiYsSI1riTmaDhouFJBU8DRohCTqIqKlRcIxWuUp0hQlUhQQyClixKLodcKEhnI2SZISZUWwXRshKag6Qkcx0LBpCZmIJUYBZFAdeMIKXAVFyXqotqgOy75bJpkqhqhRSDRgWaCbseJJ5K02CYRNeY10kzpSDaQExEiiosiLWxhU69H2RvVUKVCStYyIa4RU3P0mAoFS2A4WbSYTeOkOnbuyuCYOoYbp1ZVSCiSvbaDpQlq9BjSVXCVXiY0qKgkcdwGunIGhWyR8aMUuh04KnkEvVozBcclYmUxXYUiEewiRBSViK4Tj9Uy76KTSCYmfaB5r8SHXWQPm6IYN24c69atC153dHTQ2NhYcbyjoy941tnZWXF8ODn1pE9w5plnDvt1zvkLvrt+/fqhZTzrLzj5R8gB5RwhfBzk/DjICKGcg3Lyh/vaQBkH93YcaoYtmH3eeeexevVquru7KRaLrFixgosuuig4PmHCBGKxGOvXexueL1u2rOJ4SEhISMjIYNgUxdixY1m4cCFz585l9uzZzJw5k9NOO42///u/Z9OmTQDcfffd/OQnP+HSSy+lUCgwd+7c4RInJCQkJORDMmyuJ4BZs2Yxa9asivceeOCB4O+TTjqJp556ajhFCAkJCQn5CzksK7NDQkJCQt4/oaIICQkJCRmSUFGEhISEhAzJsMYohotSjaBlfbDma+WY5uDFWyOJj4OMEMr5UfJxkBFCOT9KDqaMpTnzg9ZZD1tl9nCSzWbZunXroRYjJCQk5GPJiSeeSHV19fv+/MdSUQghyOfzRCKRD1zNHRISEnK4IqXEtm1SqRSq+v4jDx9LRRESEhIScvAIg9khISEhIUMSKoqQkJCQkCEJFUVISEhIyJCEiiIkJCQkZEhCRRESEhISMiShoggJCQkJGZJQUYSEhISEDMlhpSieffZZLr/8cqZNm8bSpUsPtTgVXHfddcyYMYMrr7ySK6+8kg0bNowYeXO5HDNnzmTv3r0ArFq1ilmzZjFt2jTuueee4HObN2/mqquuYvr06dxyyy04zhAbDR8EOb///e8zbdq0YEz/8Ic/DCn/cHPfffcxY8YMZsyYweLFi4eU5VCO5WByjrSxBLj33nu5/PLLmTFjBg899NCQ8hzK8RxMzpE4nkMiDxP27dsnp06dKtPptMzn83LWrFly27Zth1osKaWUQgh5wQUXSNu2g/dGirxvvvmmnDlzpjzllFPknj17ZLFYlFOmTJG7d++Wtm3L+fPnyxdffFFKKeWMGTPkG2+8IaWU8vvf/75cunTpIZNTSilnzpwp29raKj43lPzDycqVK+WXvvQlaZqmtCxLzp07Vz777LMjbiwHk3PFihUjaiyllHLNmjVyzpw50rZtWSwW5dSpU+XmzZtH3HgOJueOHTtG3HgeiMPGoli1ahXnnnsudXV1JJNJpk+fzvPPP3+oxQLgvffeA2D+/PlcccUVPPLIIyNG3ieeeIIf/vCHwX7mGzdu5Oijj2bixInous6sWbN4/vnnaW5uxjAMzjjjDACuuuqqgypvfzmLxSItLS384Ac/YNasWSxZsgQhxH7lH27GjBnD9773PaLRKJFIhOOOO46mpqYRN5aDydnS0jKixhLg7LPP5uGHH0bXdbq6unBdl0wmM+LGczA54/H4iBvPA/Gx7B77YWhvb2fMmDHB68bGRjZu3HgIJeojk8kwefJk/vVf/xXbtpk7dy6XXXbZiJD3xz/+ccXrwcaxra1twPtjxoyhra3tkMnZ2dnJueeeyw9/+EOqq6v5+te/zlNPPUUymRxU/uHmhBNOCP5uamriueee49prrx1xYzmYnEuXLuW1114bMWNZIhKJsGTJEv77v/+bSy+9dMTem/3ldBxnRN2b74fDxqIQQlQ0EJRSjpiGgp/61KdYvHgx1dXVNDQ0cPXVV7NkyZIRKe/+xnGkje/EiRO5//77aWxsJJFIcN111/HSSy8dcjm3bdvG/Pnz+e53v8vEiRNH7FiWyzlp0qQROZYAN910E6tXr6a1tZWmpqYRO57lcq5evXrEjuf+OGwUxbhx4+jo6Ahed3R0BG6KQ826detYvXp18FpKyYQJE0akvPsbx/7vd3Z2HlJ5t2zZwvLly4PXUkp0XT+k98H69eu5/vrr+ed//mc+97nPjdix7C/nSBzLHTt2sHnzZgASiQTTpk1jzZo1I248B5Pzf/7nf0bceB6Iw0ZRnHfeeaxevZru7m6KxSIrVqzgoosuOtRiAd7+GosXL8Y0TXK5HE8//TR33XXXiJT39NNPZ+fOnezatQvXdfn973/PRRddxIQJE4jFYqxfvx6AZcuWHVJ5pZTccccd9Pb2Yts2jz/+OJ/97Gf3K/9w09rayoIFC7j77ruZMWMGMDLHcjA5R9pYAuzdu5dFixZhWRaWZfGnP/2JOXPmjLjxHEzOs846a8SN54E4bGIUY8eOZeHChcydOxfbtrn66qs57bTTDrVYAEydOpUNGzYwe/ZshBBcc801nHnmmSNS3lgsxp133sk3v/lNTNNkypQpXHrppQDcfffdLFq0iFwuxymnnMLcuXMPmZwnnXQSN9xwA1/+8pdxHIdp06Yxc+ZMgP3KP5w8+OCDmKbJnXfeGbw3Z86cETeW+5NzJI0lwJQpU9i4cSOzZ89G0zSmTZvGjBkzaGhoGFHjOZicN954I/X19SNqPA9EuB9FSEhISMiQHDaup5CQkJCQD0eoKEJCQkJChiRUFCEhISEhQxIqipCQkJCQIQkVRUhISEjIkBw26bEhhx979+7ls5/9LCeeeCLgVZWnUinmzp3L5ZdfPuzX/+1vf8uSJUs47rjjePDBB4f9eiEhw0WoKEL+qonH4yxbtix43dzczPXXX4+maUyfPn1Yr/3MM8+wcOFCrrzyymG9TkjIcBO6nkIOKyZMmMBNN90UrPB37tzJV7/6Vb74xS8ydepU/uEf/gHTNPnd737HnDlzgu+1tLRwwQUXYFlWxfmy2Szf/va3mTlzJrNmzWLx4sU4jsMdd9zBpk2buPfee/nlL39Z8Z1FixZV7DWwbNkyFixYAMALL7zAF77wBWbPns2cOXN44403AK/txD/+4z/ypS99iUsuuYTrrruOrq4uAC655BK+9a1vcdlllwX7GoSEfKQc5LbmISEHjT179sgzzjhjwPtbt26Vp59+upRSyjvvvFM+88wzUkopLcuSM2fOlM8//7w0TVNOnjxZbt26VUop5U9/+lN59913DzjXd7/7XXn77bdLIYQ0TVPOnz9f/uIXv5BSSnnttdfK5557bsB33nnnHXn++ecH+49cc8018uWXX5Y7d+6UM2fOlN3d3YGc559/vszn8/KXv/xlcF4hhPza174mH3zwQSmllFOnTpX33XffXzRWISFDEbqeQg47FEUhHo8D8J3vfIeVK1fywAMP0NTURHt7O4VCgWg0yhe+8AWefPJJ/uVf/oWnn36aX//61wPO9fLLL/PYY4+hKArRaJQ5c+bwq1/9ihtuuGG/1z/55JM58sgjefHFFzn22GNpb2/nggsu4NFHH6W9vZ3rr7++Qtbdu3czb9481q1bx0MPPURTUxPbtm3j9NNPDz73mc985qMboJCQfoSKIuSwY9OmTUGA++abb8Z1XS677DIuvvhiWltbkX5Xmzlz5nD11Vdz9tlnc8IJJzBx4sQB5+rfGloI8b622fzKV77Cb37zG4455hi++MUvBu2wJ0+ezE9/+tPgc62trTQ2NnLXXXexceNGPv/5z3POOefgOE4gJ0AymfzQ4xESciDCGEXIYcXOnTv5z//8T+bPnw/Aq6++yoIFC4IsqA0bNuC6LgDjx4/njDPO4I477uDLX/7yoOe74IILeOSRR5BSYlkWTzzxBOedd94B5Zg+fTqbN29m+fLlfP7znwdg8uTJrFy5kh07dgDw0ksvccUVV2AYBq+++irz5s1j9uzZjBo1ilWrVgVyhoQMN6FFEfJXjWEYQdaRqqrEYjFuvvlmLr74YgAWLlzIggULSCaTVFVVcdZZZ7F79+7g+1dddRW33347U6ZMGfT8ixYt4t/+7d+YNWsWtm1z4YUX8o1vfOOAckWjUaZPn05nZycNDQ0AHH/88dx2223cfPPNwR4FP//5z0mlUixYsIDFixdz7733EolE+PSnP10hZ0jIcBJ2jw0J2Q9CCG677TaOOOKIIWMOH4ZCocC1117LrbfeGuzlHBIyUgldTyEhg5DL5TjnnHNobW39yPcueOWVV7j44ou58MILQyUR8rEgtChCQkJCQoYktChCQkJCQoYkVBQhISEhIUMSKoqQkJCQkCEJFUVISEhIyJCEiiIkJCQkZEhCRRESEhISMiT/H0vTqKs81kHgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# version 1 - Seaborn plot with Seaborn HSLuv color palette\n",
    "fig, ax = plt.subplots()\n",
    "sns.lineplot(x='DAY_OF_YEAR', y='PRCP', hue='YEAR', palette=sns.color_palette(\"husl\", 10), ax=ax, data=df)\n",
    "ax.set_xlabel('Day of year')\n",
    "ax.set_ylabel('Precipitation (inches)')\n",
    "fig.savefig('precipitation_by_year_v1.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>Seaborn with a hue allows us to avoid a loop.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M+7k8Hk9A6w1/rrzySgDS4Oj333/Hli1bcPfdd6NJkybqvg0bNkRxcTEcDgfGjh0LAPjhhx90x//LX/4Ch8MBQgheeeUV9O/fP+S5vV6vob+L2X7zUNQGObUyXrpcCgD47bffkCuerymRAoioKDiOQ/PmzbF7927cdtttsNlsIIREdXD/1spaCgsLkZOTg1GjRgGQNKjNFlvrqW7duiE1NTWmfRS5+vbtG/N+iaQ2yAiYU878/HxdS/E7rx2NQW1vr9E244A0aHrkkUfw1FNPwWq1oqCgQJUpNTUVHMfpZLTZbEhNTdUt83q9mDp1KgCp55Pdbg96rpSUlIAklHhhxt88GLVBTn8Zm/yaC5w9j3bt2qNv7/jHnzweT5UG2BEL7lJSUrBixQrs3bsXN954Iz788EOkp6dHPHCw1spa0tLSMH/+fJw5cwaUUqxatUoX6GYEhxCKA9uPo+R8RU2LwoiB3Nxc3HfffXjyyScxYsQIZGZmxjwfhcPhwAMPPABBEPD666+HVBKM2otZg9kRh/Bz587FW2+9hX//+99o1KgRcnNzowqihWqtvH37dkyaNAndu3fH7Nmz8fDDD4PnefTp04fNBRwFgleAx8Hj9IFzaJLZoKbFYUSBMmhatGgR/vSnPwHQz0fRunVrbN68GUOGDAl7HCWeN2vWLFgsrKlCMlLr0mMVOnTogGeffRanT58GpRRz5syJyqII1VpZ8b8CwMCBAzFw4MAYRWZIsGkzawvxmI/i8OHD2LZtG6666iqMGDECgDTPtjbOx6j91Nqsp/379+PRRx+FzWbDRx99hGHDhuH1119Hnz59EiEfwx+T3UCMyMRjPoprr70WR44cMUZAhmlQZ7gz2YMe0X6dN28e3n33XTRu3BiZmZmYN28e5s6dmwjZGOFgBgWDkYQo6bE1LIYfERWF2+3GVVddpX4fMGAARAPSDBkMBqOu43M9mUtTRFQUNpsNZWVl4DhpCHvy5EnDhWIwGIy6CKmN3WMB4OGHH8b48eNx8eJF/Otf/8KuXbswe/bsRMjGCILZbiAGgxE/am0w++abb0aHDh2wa9cuEELwyCOPoGPHjomQjcFgMOoUvmC2uYiqFDotLQ29evUCpRRutxuHDh0K2xCQYSBmu4MYDEbcULKdal3B3aJFi/DOO++gefPm6jKO47Bt2zZDBWOEQrqBWNJT7SIebcb37duHF154ATzPIysrSy2CZSQRtdX19Nlnn2H79u06RcGoOcx2AzEiE2xuls2bN2PBggVYuXIlrrjiCjz44IPYtWsXsrOzUVRUhJkzZ2L37t244YYb1OM8/fTTeP3113HVVVdhwYIFeOutt/Cvf/2rBq+MEW+ISYMUERVFkyZNmJIwE8r9w0yKWkM82owDwJYtW2C328HzPIqKitC5c+eEXwvDWKjf/2YhpKJQZq+79tprMWfOHAwdOlTX3ZXFKGoGyjRFzJCjR2HPPwzBgPkoLFd3huXqq8NuE2xulvHjx+uaALZs2RLFxcUAgAceeABAYItsu92OI0eO4P7774fNZmPWRBJiUoMitKJ47LHHdN+3b9+ufmYxihrEZDcQI3q0c7NYrVacOnVKXUcpVWuVwtG5c2fk5OTgo48+whNPPIGPPvrIQIkZiUZxPdWaYLZWMTgcDmRkZMDj8aCyshLNmjVLiHCM0ETxTmHIWK6+GnxWVo3OR+E/N8vevXtjajPu8Xjw/fffq40D77jjDvz73/82XG5GYjGr6yliZfaWLVvUbpWFhYUYMmSITokwEovZSvsZkQk2N4u2zbgoiti8eTNuvPHGkMew2WyYNWuWOunMF198wRpzJiHK8222xzxiMPuNN97A+++/DwBo37491q9fj3/84x+45ZZbDBeOEQST3UCMyMSjzbjVasWiRYswY8YMiKKIVq1aseacSYgaozDZgx5RURBCkJmZqX6/4oorop4KlRF/zHX7MKIhHm3GAaBfv35Yv359/AVkmAYa8MEcRHQ9NW3aFB999BEEQYAoili7di1Ll61JzGaTMhiMuEGIOYPZERXF7NmzsWbNGvTo0QM9evTAmjVrMHPmzETIxgiCuW4fBoMRT8wazI7oemrXrh3Wr1+PsrIyWK1W1K9fPxFyMULByigYjKSl1tVRKFy8eBEfffQRSktLdctD+VwZBmO2O4jBYMQNWlvno5g8eTLS0tJw7bXXRlUQxDAW3w3EfgsGI9lQXU8mGxBGVBTnz59Xu17GSrCOmVry8/PxzDPPwOFwoF+/fpg1a5auTQgjCOa6fxgMRhwhJq2jiBjMvvLKK+F0OmM+sLZj5saNG3Ho0CF89dVXum0mT56MGTNmYOvWraCUYs2aNTGfJ9kQeBEXz5SGXK+MNJg9wWAkH7W2jqJly5YYPnw4rr/+eqSlpanLI8UognXMLCwsVNcXFBTA7XajV69eAICRI0diyZIluOeee6p0IclC6fkK/Lb/HLhWTBUkE/GYj0Lh8OHDGDNmjFqlzTA3JU4vGqTZYbNEfqZ9ridjZYqViIoiKysLWVlZMR84WMfM1atXq8uKi4t1vW1atGiBoqKimM5RnQfFvzOnWaAOK4BUgAaXkXosANLg9rhNcw1mkUPBZrMFFK8BgQVtieKHH37Ad999h1WrVgEAHn30Uaxbtw5LlizBihUr0KpVK0yaNAm7du3CjTfeiAsXLmDOnDn48ccf0bt3b53cLpcLs2bNAs/zYa/H6/Ua+ruY7TcPRU3LSQAc51qiJa1AY7iCbqOV0SF7bwoKCpGbW54IEaMioqJ49NFHq3UCbcfMdu3aqcsJIbrgeLTdM7V069YNqampMcuUm5uLvn37xrxfIig+VYLTB88DQFAZy4orcfTSGaSlpaF735pv9W7Gv2V+fr6uAaDjk7Wo+PBDWAxoM55x113IGD0q7DZt2rTBtGnT1Pklrr76apw7dw7t2rXD1XKL8hEjRqjzUaxevRoDBw5E8+bNkZqaqruWefPm4f7778fjjz8etslhSkoKevbsGYcrDMSMv3kwzCCnRyA4frAQV2a1QZdWDQLW+8uY/v13QFkFMq+4An37hm9fXyV5PJ4qDbBDKoqxY8di9erV6N27d9AX+E8//RTx4P4dM7VkZmbqumdevHgRLVu2jEV2BqNWEK/5KLZt2wa3241BgwYlQGpGfIjNh6TEJkzmeQqtKBYvXgwA2Lx5c5UOrHTMXLRoEf70pz8FrM/KykJqaqqqUTdt2oT+/ftX6VzJRfhbxGy+y9pAxuhRwO231WibcaB681FcuHABr7/+Ot59913jBWXEDV9wOjpIbSu427FjB0aPHh02PvHxxx/jrrvuCrouVMfM7du3Y9KkSejevTsWLFiA6dOno7KyEl27dsW9995bjUtJDmjEGn6T3UGMqKjufBTffvstSktLMW7cOHXZsGHDsGrVKtYtwcT4mvxF99zSyC+AGiGkohAEAXfddRfuuOMO3HzzzbjyyisBSNlK3333HdauXavOUxGMUB0zx44dq36+5pprsHbt2urIn3xEuD/MNtJgRCaYda2dj6J169bYvHkzhgwZEvIYo0ePxujRo9XvnTt3xqZNmwyXnREfon1sa10Lj7Fjx2LAgAFYvnw5li5dioqKCgBAw4YNkZ2djcWLF6N169YJE7SuEPH+kDdgVfK1h3jMR8GoncTqelK2N1v32LBZT1deeSVmzZqFWbNmoaSkBBzHqZkbDIOIcIOYrRCHEZl4zUeh5ciRI/ERjmEosT6vtS6Y7U+TJk2MlIMRLWa7gxgMRkSiNRDMGsyO2MKDkVhMGstiMBhVwPfCjy2YbbbHnykK0xF+wgmzdZVkMBihibUlh1m7xzJFYTIi3h/mun8YDEY4YrQQal3Wk8LXX3+NF154AWVlZaCUqoVB0VRmM+KPev+wpCcGw/TE+r73uZ7MpSkiKor58+dj6tSpbOKiRBEpRmG2oQaDwYhIbQ9mR1QUSt0EIzGYbSTBiA/xaDO+YcMGvPzyy2jWrBkA4H//93/xxBNPJPZCGDER6/wSanqsyV4DERVFz549sWPHDgwYMCAR8jCirMxmtl3tQTuJF8dxeOCBB7B582YsWLAAK1euxBVXXIEHH3wQu3btQnZ2NoqKijBz5kzs3r0bN9xwg3qcvLw8TJ06NWwFN8NcxO56qtp+RhNRUezYsQMffPAB7HY77HY7i1EYTcRgttluIUYkgk3iderUKbRt2xZt2rQBAAwdOlRtM/7ZZ5/hz3/+c0Bx68GDB3Hq1CksX74cnTt3xrPPPotGjRol/HoY0ROrhaC4nmpVZTYA1q0ywUQyUU12/9QK6Pm9SCnYDWKJf5Ifd8UfwWVeH3abeLUZb9GiBSZMmIA+ffpg4cKFmD17Nl5++eV4XQrDCGJ+Xs1pUkQ1w92WLVvw/fffg+d53HTTTRg+fHgiZKubRFtwxxILah3VaTMOAK+99pr6+YEHHsCtt95qlKiMOBFr/SyJMaaRKCIqirfeeguffvopRowYAUop3nnnHRQWFuIf//hHIuSrc0T2PJnrBqoNcJnXw9uga43OR1HdNuMVFRVYt24d7rvvPgDSfWA1YMY+RnyJtYBOTY812WMeUVFs3LgRq1evVnvejxo1CmPGjGGKwihMdoMwqk882ozXq1cP//nPf9C7d2/07NkTH3zwAbMoagMxPs+1snusgnZilAYNGsBmi7qXICNmWIwi2YhHm3Gr1YpXXnkFzz33HNxuN9q1a4d58+YlQnxGNYi1GyyJsZI7UUQVo3jvvfdwzz33AABWrVqlTmLEiD8+RRDCX800Ra0jXm3G+/Xrhw0bNsRfQIbxxNzryTBJqkTENJBZs2bh66+/Rq9evdCrVy/897//xYwZMxIhW90mwo3CYtkMhvmJuS7CnElPkS2KVq1aYeXKlXC5XCCE1PgE9UlPpImLzHYHMRiMkPiynqJ7cNXYhMke9JCKYu7cuXjmmWfw0EMPBV3/xhtvGCZUXSby7WGuG4jBYIQhxve+shkx2WMeUlEo2RkDBw6s1gkqKytx991344033giYY3vp0qVYt24dGjZsCAAYM2YMxo0bV63zVYcd6w9BFAhuGdO9xmSItoUHg8EwP7HWQ/iC2eZ60EMqiltuuQUAcPr0afzzn//UrZszZw5GjBgR8eC//PILpk+friss0pKXl4eFCxeid+/eMYhsHGteyQGAmlUUkTDX/cNgMMIQ88RF5vQ8hVYUS5YsQXl5ObZs2YLKykp1Oc/z2LlzZ8gsDi1r1qzBzJkzMWXKlKDr8/LysHz5chQUFOC6667DU089hdTU1CpcRvJAI0a/THYHMRiMkMT6wjfrTMghs5569uyJxo0bw2KxoHHjxuq/zMxMLFiwIKqDz507F/369Qu6zuFwoEuXLpg8eTI2bNiA8vJyLFu2rGpXkWBe++Y4Xv7vEWMOHq3riaU9MRi1hqjbjJu0MpujEWrLDxw4gB49elTrJLfccgvef//9gBiFlsOHD2PatGnYuHFjxON5PB7k5eVVS6ZgvP2E1BF3wqI+Ybe7c+15AMC6UZlxl4GUpgBOG7jGHnD1xMD15Xag0g7YRVhaeOJ+/mTAZrPhqquuqmkxdCxfvhxfffUVAOCmm27CP//5T/zwww94+eWX4fF4kJ2djUceeUS3z7PPPovrrrsOd9xxBwCpzcfzzz+PCxcuIC0tDXPnzg1Z03T8+HEIgmDsRTEi4kAKCixN0IC6cQUti7j9mHXnIVJgwB/SMOn6xhG3ryrdunWLyXsT1cRFc+bMgdPpBKUUhBCcPn0aH330UbUELSwsRE5ODkaNGgVA0qSxVnzHerEKubm56Nu3b8DytyEpimDrdKz9PLrtqsBvvxTi4u9lIY9/9tdinDt2CRkZGbi2b7e4nz9WQv0ta5L8/PyANG6Hw1Fjqd05OTnYu3cvNm3apM5H8c0330Q1H8VNN92kyv3II49g4MCBGDt2LFavXo3XXnsNr7zyStBzpqSkoGfPnoZcjxl/82CYQc7CMhcKTl5CkyZN0Ld94OAlQMb1WwBK0bRpM/Tt2yvu8lR1kB3xzfzkk0+iW7du+PnnnzF48GB888036Nq1a5WE1JKWlob58+fjj3/8I1q3bo1Vq1ax3jVR/9XiAAAgAElEQVRAFF0B5f9MZpqamv3vIy13BWAxoIle7wlAr3vDbhKP+SguX76MX3/9Fe+88w4A4M4771QzExnmJfbusebMeopYme1wODBr1izcdNNN6N+/P9555x3s37+/yiecOHEiDh48iKZNm2L27Nl4+OGHMWjQIFBKcf/991f5uMkCjXBnmev2YURDp06d0KuXNDpU5qPgOC7sfBSjR4/WHePMmTO48sor8dJLL+HOO+/EpEmTYLfbE3cRjCpR5RnuTPagR7QolFFN27ZtcezYMfTo0SNi33x/tm/frn5esWKF+nngwIHVrtOoc5j1TjIzve6Fu9OdNd5VoDrzUQiCgMOHD+Oxxx7D008/jU8++QRTp07FypUrEyA5o8qoj2sUpbSabcz2dEe0KNq2bYu5c+eiT58++OCDD7By5UoWJJMRRBL3Y/puluAvDaYfaie5ubm477778OSTT2LEiBHIzMyMaT6KFi1aICMjAzfffDMAYMiQIThw4IDhcjOqRyyuJ+2zbbY24xEVxXPPPYd+/frh2muvxejRo7Fnzx48//zziZDN9Dj5wKykRGGu24gRDmU+igULFmDw4MEA9PNRiKKIzZs348Ybbwx5jD/84Q/IzMzEjh07ACBusUKGsUSsiyIC4KkM2MRkeiKy62n58uVqZfY999yDe+65B3PmzEGfPuFTSOsCLq+Ihmlx9hNHnuIuuu0YpiEe81EAwKuvvoqZM2di/vz5qF+/fkAbcoZ5CfW4dtz3FPD5DuA5ajorQouhldnJjsMTfxecmu3AgtlJQ7zmo+jQoQOLSVQTWnIM9Ph6cH3+Bc5qfDJAqOf156JcXN30GjQu2uHbVrOx2aY8DqkoevbsiYMHD6qV2QpWqzXqyuxkxxDXU9Tpsea6kRiMWoHjPOA4BwhOwNrI8NPRIM9rqbsUE7+8D/+v9QAs0myoTYk129MdUlEMGDAAAwYMQP/+/atdmZ2suLyJj1EwBcFgVAc5ASXBz5H2bB7RDQD49VK+b6HIg1JfnY/Z3FAR56MI1X8pmeejiJSqqGCI6yna+8Nc9xGDUTuIVKgU79MFmTObC5bRKHpAuXq+/Uz2fBs+H4XpqSwGPh4JDH9XXUQJBWeNrChchrieIgSrTXYDMRi1Ckr0/xt9uiB6SRmE6qqvBQ+ILd23XwJki4WI81GMGDECJSUl2LdvHywWC66//no0aNAgYQIazqlvgTO7gC8fBzAcACCKNGi3h4JSFz784bT63VkTrid1hGK2W4nBqA0kVlEoBHtaqVYG0QNq064zXKSYiFhHofSfee+99/Cf//wHt956K/bs2ZMI2RJDvWbS/8e3qotIiEK6Jz7ej9e+PaF+d9aE68msDesZjNpAwl1PgZ9EWUHoBnuiV1+ZbTJNEbGOYtGiRfjggw/QuXNnAMChQ4cwffp0bNiwwXDhEgKRrQLqsw5EIbii8PopEGMK7sLfICa7fxhRsnTpUnzxxRcApESRKVOmICcnBy+++CI8Hg9uu+02/P3vf9ftM2XKFNxwww0YOXIkLl26hAkTJqjrKioqUFJSgp9//jmh11HrUV27iXI9Bc4vQeR3DQVAOSs4KkquJ116bELEi5qIFkVaWpqqJACga9euMfd6MjU0yJwPYvBfyWbRX7chrqcYp0xkmJ+cnBzs3LkTGzZswMaNG3Ho0CFs3rwZ06ZNw7Jly7Blyxbk5eVh165dAICioiI89NBD2LrVZ+U2a9YMmzZtwqZNm7BhwwZkZWVh9uzZNXVJtZiaz3oiikVBKSgn+7hFj9/25nrAIyqK/v37480334TT6YTH48HHH3+MTp06oaysDKWlpYmQ0VhI4MteDOF6svopCiPSY31z7Ibq9cR8T7UNbZtxu90e0GbcZrOpbcYBqG3Gb7vttqDHW7duHdLT0zF06NBEXkZS4IsLJMj1FOQ0RON6opzs1BE8fq6nREgXPRFdTytWrIAoili4cKFuuTIJS35+fog9awlBLYrgisLfonB4DWiOyGIUcefimVIUnboMqzX+81E0b9MIzduEn4msU6dO6melzfj48ePDthkHpEaC/oiiiDfeeKPWTBtsOhLsevI/LeBzPQEAtVgBEYDo53pKnGhREVFRHDp0KBFy1BxBLIpQrierRTLAUixSJjRvRPfYSDGKIHnZjNpBddqMK3z//fdo166dzh3MiIUEp8cG+UQ0XgHV9eRnUdSagrtNmzZh2LBh6oxa/iTNJENBbpjQrifp/7eva4tUqwWrXQbMWR2tRcGImuZtGiO9qb1G56PIzc3FpEmTMG3aNAwePBh79+6Nqc24wtdff43bb7/dSFGTm0RnPdHAgZ1iURBKADVG4dVZFGZ7zkMqitOnpXqBo0ePJkyYGiGGYLZiUaTKGkMgNfBr+oIYiT83o0oobcYXLVqkFrJq24y3bt0amzdvxpAhQyIea//+/Zg4caLRIicviS64U/7XZsKqwWz4YhSip3b2epo0aRIA4MUXX8SPP/6I6667DqWlpdi3b1/Edsi1imDB7BDpsf4xCtEARRGpfz1zPdU+4tVmHJCmRM3MzDRS3OSG1nzWE710GaleCmrXu560G9XKOoqffvoJK1euhNvtxptvvomjR4/iH//4RyLkM54Ygtn+WU9GxChYMDv5iFebcQD45Zdf4itcnSPRrqfAZfX/9y7Ma2nBtMn1QS0+i8LMweyI6bHbtm3D22+/DQDIzMzEBx98gC1bthguWMKIIZidEIuimusZDEYYEl1w53dahbbFRF9HIehdT2YLZkdUFDzPw273TfBht9uTsuBO50OM0qKomRhFYKWnuooQ0GSobWHUSiiloI5zNS1GeBIcowgXPKfQF9zpJy4yXrRYiKgo+vTpgyeffBK7d+/Gnj178PTTT6Nnz55RHbyyshJDhgzB2bNnA9bl5+dj5MiRGDhwIJ555hkIggE1CdGgFr/4lEC0rifBENdTdC08SBAlRU+fhrj2E1CXK/5yMRiRqDgN+uO/QSsLa1qSMIQvuDtZnotL7sD3VVUJ5+iigC/rSfDorAiT6YnIiuLZZ59F8+bN8eKLL2LevHlo1qwZnnnmmYgH/uWXXzB27FhdrriWyZMnY8aMGdi6dSsopVizZk3MwscFolgUFs2i6FxPRlgUkbL3BCLd6EHjIx6PdACvN+5yMRgR4Z3S/4KJByoRXE+HLm9HzvnVhp1Wv4xKBXeA1BRQtzLuIlSLiMHsevXq4emnn0ZZWRkaNYp+6sA1a9Zg5syZmDJlSsC6goICuN1u9OrVCwAwcuRILFmyBPfcc08MoscJJadZY1GEynqymMD1FNZiJjWT0cFgANC4dRLffj9qEpz1FLF8NpTryWSaIqKiOHnyJB599FFUVFRg7dq1uO+++7B06VJ07Ngx7H5z584Nua64uFhXXNSiRQsUFRXFILZEXl5ezPsoKO0RWpw+hT9Ab1EcOXIUTmugPBculOm+l5dXBm2zUB2IMw2KoRfs2N6KFNhgA6UkYH2zC8VoDeBwXh7c6ekB+xpFvP8G1cVmswVkEAGBWUVmJF4yer1eQ3+XYMduxP+ODgCOHT2CCluFYeeOBX8527ouoimA4yeOofy0O3CHZsH3qyqXkAFY6sPtdkvHpBSt5HUiEcHJiut8wWkctBxU9yuviP+7pTpEVBRz5szBM888g/nz56NVq1YYP348ZsyYgVWrVlX5pIQQXUA82vYF/nTr1g2pqakx75ebm4u+fftKX7zfAYcAomnC16F9R/To2zZgv2anDwAnz6jfU9PTfceJE3k7TsLFewBwQY994Lvf4Clzw8JZAtaTvIMgZ87g2i5dwDVrFle5QqH7W5qE/Pz8gCpsh8NRo5XZ0RBPGVNSUqKOJcZKqN+cFltBD+9Ap6s6gmt2rSHnjoVgcpLDh4Hi33BVhw7gWvQI2Kfw1HYAiNs9ffBcGS6dr0Bqair6du0LKoookNdxHKdaNpnNm6Br127Al98CADIyMgx5rjweT5UG2BFjFKWlpbjxxhvV7+PGjUNlZWXMJ9KSmZmpa19w8eJFtGzZslrHrDIxBLNFP3PVENdThEMqhThWrxve/fv1K4ka6Y6/XIxqsXTpUgwePBiDBw/GvHnzAEjtx4cOHYrs7GwsWrQoYJ8pU6Zg/fr16vezZ89i3LhxGDZsGP7617+ioKAgYJ8apYaK2WIiTNaTEUVuAfWzmqQdCirNRQEEBrNN9ieMqCgASQspI/4LFy6AVPNFlJWVhdTUVNW02rRpE/r371+tY1aZIMHsUOmx/osNjVGEOLTvlBwcqz7026cWPKh1kHjMRwEAixcvxuDBg7Fp06aQyqVmMS5G8cmvH2HdkcCEl39/+Su++bU4hiNJMgokMOGDwMhpA+T/tYqCarYQPfrq7bhLUj0iup7Gjh2L//u//8OlS5fw8ssv4/PPP1fbIMfKxIkTMWnSJHTv3h0LFizA9OnTUVlZia5du+Lee++t0jGrTZBgdqisJ/+UVCPSYyONatT1HAfq9vivlP5nFoWOH748ip2f5RvSZvxPt1+NPw66Ouw22vkoAATMRwFAnY8iOztbnY+icWN9+3JCiGrNu1wupKWlxf16qoWBcz18cfJz2Cw23Nl5jG75h3t/R7mbx83XROmRkJ+RIyXfo1vm9X6rjOgG7YeuDEBjUQRMhRp3UapFREUxevRotGvXDt9++y0EQcDzzz+vc0VFYvv27ernFStWqJ+vueYarF27NkZxDUC1KKJzPWlNsBptCshxoB4/RaHIk+Be+4zwxGs+iscffxx33303Vq5cCZ7n8fHHHydA+hgwMOtJpCK4IJN5iYTG1iFBllEkgYFsYkS2ll/3WOrnelL/ZoJ/wZ25NEVERfG3v/0N7733Hq677rpEyJN41Ja/GtdTiPRYkVBYNUF3I1p4RBujAGcJUBTKiIgSbcSF8cdBV6Pb/8uq8WB2deejeOqppzB79mz85S9/wdatW/Hoo4/i008/NU+nBANdn4SKEEngdQqEgA/hAQiO0tkgWKW0gRaFcj6d64mqWU9mdz1FjFFUVFTA6XQmQpaaQbEotHUUIW48SVH4vgvydvTCBZA4TfAUceIi1aJAoEXBXE+mJTc3F/fddx+efPJJjBgxIiChI9J8FJcvX8bJkyfVDrMDBw7EhQsXUFJSYrjsUWNgewyRiEFH/IQAYiz3exj3mCEWhd/ZqKCZ3Q4UalwnIJhtLlUR0aJIT0/HzTffjM6dO6NevXrq8jfeeMNQwRKG2hs+OteTtjqbyjcoOXoU9NhRWLp2jYM8fv/7r9ZoCur2M59V15O5brK6Tjzmo2jSpAlSU1Oxb98+9OvXD7m5ucjIyEDTpk0TdRmRMVBRCFQEgrieBEJicwGHkZEYEaMIyHridet1FoWJu8dGVBSjRo1KhBw1RxDXU7hgtk1j5qsDGUriNoqP9I6ngGT7WCygHj9Fodx0zKIwFfGYj4LjOCxduhTPP/883G43MjIy8OqrryZC/Ogx1KIQAtyphFAQGqsL2DcS83f3xcv15M07BHvXa8FxXJCsJz+rRRuj0CyuifBnOMIqiqNHjyIjIwM9e/ZEq1atwm1aewnqegoXo/B9p1pXT6Jeztp5dVkwu1YQr/koevTogU8++ST+AsYLIxVFkGC2UtcUU4xC3oejFJphF4D4uJ5cX2/Dpb/dhyaLFiJjzOjA04eyKAgPN69xS5nMKxAyRrFu3TqMHz8eK1aswB133IGdO3cmUq7EQYM1BQzterLqKsqVHQhAaXx+3DDHKPWcA4EmGObf/E9VXOa6yRh1BeL3f/wQiQjRb+4YxZKoSoyCA0D85IyH60n8/XcAgPfAAel0ataT/L/g/8zKHg1RwMjXcwAAqnf72JdA8eFqyxQPQloUK1euxGeffYZWrVrh559/xqJFi3DTTTclUrbEQETAmhp1HYXO9aSOBjSxgWpmoIR7xR8p3QWK9gCkWFFgMDvBvfYZDC0G3n+EihD9LAolNlGVGAUHOUtQ5yGovkXBKbUt8rPpy3qS/iO8n0WhKBDRt9xq4aQx36rbpAXP1fzAL2zWk+Ju6t27t7myK+IJJYA1RV+ZHSo9llJYNX8xCzipCI/EMTagOjWD5IxTUadJaKVfNhpJHouCEgqRr/qDazbTPZHU2LUbHMwWiX7OGp9FEXuMgqOBGYb+FkaVkHvPqYkmfsFsrUKQF0j/aa5Nim2Y6/4NqSj8c7ONqGo1BVSUFEUUvZ4Igc71ZOEAXhufiIOiCHeD+FeOUqdTV8CTTBbFb7+cw09fHq3Svna7Ha46PHmTy+XSzUqZKKihwWxRynzSoHRGEGKKUSgWBQ14nsLFKAgVcbzsB4g0/ARrikWhKAr/JEbi5y7mFKXup0DMNtaLqtcTEKg4kgYiAhYriMYLF7KOgupdT1aOk0Yz8cw2CnODUPilCHIcaIWmnXMSxSgunZVauldldNyyZUsUFBTA6XTWKcuCUgqn04mCgoKaabJp4HzUIhVCxiiEmGIUmloFPwsiXAuPEs855Jd8h8sRZr/j5ACD0l6H+pkUVPRTNIpy0lgUXoGoqfdmIWSM4siRI+jTp4/63e12o0+fPmpK2U8//ZQQAQ2HigBnBeV8I7CQwWxC0aaxb54HKyf5R1Pj6XoKA6EUeruOA6mshKVJE2UD6f8ksCgUKKHgrLENUho2bAgAKCwsBC/7hL1er9pryazEQ0a73Y5WrVqpf4OEorz0DFDOIhHVl7CCUCXXk2xR0MBBSLimgES2JCIFvJX010DXkxLMDpX15FMUVzZKgw1+LqoaJqSi+OqrrxIpR80hWxSUs2kWha7MvlKjKCwcJ5m98XQ9hSm48w+2UQsHygdxPSWBRaEQ6p1DKcHR0hx0aNgPdmtgc7yGDRvqXpa5ubmGzc8QL2qDjGFRMnyoGPcWMuHSY2MLZssxCtAgWU/hFIUyHUGEuJmsCPxdT+rpeX8F4EuPBYDFd/XCV/lF+N1kLeRDKoqsrKxEylFzyBYF4bSup9AWhd2idT1JZi+N6zwQYWIUoH4PC6ev9EzCFh40xEuggr+Eo2W7UT+lObIyrkmwVIygqC9aAywKKgJ+vZ4UF3FVYhRAkJhfGGtBcVNFbVH4ZT35Cu6CWxScbFGk2CxoJFxEP/7HsOdJNBErs5MNR6kL5FIqCKHSHNiUSA32tIoiRNYT8aujsCD+FkW4Z4z4tzHgOFCv5sZT58xOIkURwqQgav2LiednrnOojvi4H1kkYkBEVahKjELrevK3KMK6npT7LcK55BhEqKwnIoSKUUj/2ywWTDk9Do2Ei+HPk2CiDmYnC45SN+CxQvDKP1gQ11M4i8LmZ1HEO5gdbmxEQcBp0njBhbIoksn1FPxalAfWiP48jCpiUIyCUAIKGqbgriqup8CXfrh7SYkxRJrcSHEF+1xPfnEQPnjWEye7nmwWznRKAqiDikIJiJW5i1HqOedzPcEXzBb5cJXZvu8WjpNGNXG1KELf9NKNzPkyJziLPkZRS4LZx0uOYn/xz1FtG+pSlAfWiFnJagLqKUcj/nRNi1E9qDEWhai22aG6l7liSVQ1RhGY9RRabkpFtDz7K7Je/R/AE2YqaMWiUFxP2ronSvWuJ0rBqbMCSvvZwiRuHCosw0+/10w9W51VFD8WbcL35z4IblGEcj0R//RYOZc7ni6fcMFsEHDE4su55vyCY0ow0eQWxfL9r2PenrlRbRva9aR0/TW3UowWevhddHB/B+qt3nz0NYpBFoW2dkFrVajpsTHNR+FzPZGArKfQ9xKhBPUqLsHiqQBcl0NuF5D1pF0HgGhcxdqXrxKjsFpCK4r5W4/g+c0109KjzikKi/JDKL5+NT02sqIQAxRF/C2KsK4nQsDB4sus4Cx+riefwuK9YsjrqGk8ohtu0RN5Q4RWeknneuLlKntPac3KUR3iUEdBiRhQvSxolYNGaVS7hUcIi4ILkrNFQWBR5BAD59v2CRU+64lonlf1LNYUcJCsC7s19CvZLYhw19AzXecUhfKeV339lAQW3Pm3ApYhVK/x/9KqAXiPEF/XUxhUf6dys3FccNcTIVj8+GZsfmufofJUFYHwEEh0eeIhLQoo7ojkcD0hpYH0f61WFNW3rOnBN0G/n6xbJmpcQkIQiyK2poA+kz10jCJQURRXuGARIysKtY24IEiuJp3rSR/MtiorrVLbDztEWC0cBC54VT0vUvAh4qdGU/cUhb9FQcSos57824z/vxYNwF9yqgoiLtWU4WIU8oPBaRQFNMExqglmXy6qxOUic7oxBCKA9+95E4I6Y1GoiqIW91RTf4tq/CYlRwIWaesbiE5pVMGiCNvrKbRFUe7ygKNRWBRaV7DHA3+bQtsU0KLIbZMUhRUi7FYLBC540SUvELVtSaIxVFF89tlnuP3225GdnY1Vq1YFrF+6dCluvvlmDBs2DMOGDQu6TbxRWpH4LArZ9STXPFusXEhFIfi5ngC5OC+O9Qs+PRHEVykfXlUUCFFwRwlEnkDwmnO0zRMefNQWRfDlij85adJjbVIhJ3UngaKIs/LWxSVoNWMU/t1jtavUorrA4wlU9FkUYe5dKmqUmsPhNw+2PphtVy0KSTHYVIsiuKLwivL84M7LajptojCsjqKoqAiLFi3C+vXrkZKSgrvvvht//OMfcdVVV6nb5OXlYeHChejdu7dRYgSgWBSqolB6Pcnmni3FCiFsHYXfMm0wO56upyD3vjIA4XgeFEC9WzsC2v72GotCEMSQ1xFPRErx2jfH8bf/aYf6qdHdTrwYi6IIX0cRl46fZkB5AdZmiwLGKApBpxyCuZ7iE6NQayWC3FMiEaOMUWjmi3G5AOrrGqC4npTRuU05jex6skGA3cJB1Hg3wPnG8rxIAMENLO4A3P4q0POvYS40vhhmUeTk5OCGG25A48aNUa9ePQwcOBBffvmlbpu8vDwsX74cQ4cOxezZs+Hxn1/BAAJcT34Whd1ujeB68rModMHs6mV7hGtgR6mvKpuTlUOD4V0AUdMUUJN9JfCkWm26o+V0qYD5/z2CnccuRL0PH0uMIoLrKVmyntQRortuxyjUQ2meBW17cb3SUNJjYzmfEnCnAW5L7aDD/1kUiQguCkWh7eZMvXzYFh42P9eTDQRWCwfRorEoOF93N16ksAlOwFMGuBI7oDBMURQXF6NFixbq95YtW6KoqEj97nA40KVLF0yePBkbNmxAeXk5li1bZpQ4KgGuJ9WikH4QW4oVJEx6bMIsCj+kYjvp5BZe43oigRYFFSUlEatFsacgB3N3z4ppH69IYbFfhisGN5dAhPAWhbbDZ4RgdjymrzQFysvQW16zclQHNT02HrG6UO6mwKwnaawW5SAtiqwnBFkn0miznjSuYK9+HmxK9YrE36IIGqOw+KwLXiSwUPncsnJJFIa5nggh+onL/SYyz8jIwIoVK9TvEyZMwLRp0/DEE09EfY68vLyY5aI8ByBdtSgqKspBOSucbqU7pICKCgdyc3MD9uUFEW6HE8iory4rLysHEUVYAPx24gRKS6s+IpTeifXU71oZCESgvqTcOFlRUADF587gsrzd1Q4H0gFcvHABlAJlJeVBryMUG8+tx/ZLX2FQypCo97nIF6NJh/nYdcyLP9BeUe3jcDlAKMGP+36EhQscqzT/7WMg9X4AwLGjx8D9HvjicaSdBTKAogtF8JyK7hpj+Vskmvaui2gMgPc4kWdiORWC/S07OsvQEEDFwQM47tpXpdkeFSf0/p/2qe7gc+5Cdf0vB3/B+VRpwHn0rK9WYW9urq4PWyg5e4gCrJAUxfETx1HgLVPXlWYUA7Kn6KefcsFpejWXeC/DIiup40cOo6yseVD5G5w7pz7Bhw8cQNlVfQFOeqnv/2U/UkpKoOxpl5WbwyMgA5Lr6fChPFxLNJOoUQ775Wtwur1I412AHTh19hwucYm7TwxTFJmZmdi3z5eeeeHCBV2P/MLCQuTk5GDUqFEAJEVis8UmTrdu3ZCaGptmdVV4kPftSdWiqF8vDZy9HtLSMwAAGfXrgRCKvn37BuxLN36BhvXr6+IHDTLqq2ZZ+7ZtYencOSZ5tBBCkfv5rwAkM7NB5lXontVI+k48+PqodGNog9mtmjdBe1lW4eQJwO1GowZNAJQiLTU96HWE4us9X4BcIujZuydsluh+iy835oPjKNKapER9LstJDuCB7r26I80W2PlVPLkIxfLnjh2vQpPMBgHbnCijKCs5iubNm6HnNZHPm5ubG9PfItGQA7nAZcBu5UwtJxD6b0n27wFKAdvZs+iV3RHWpk1iPjb5diUAoFfP7uDs0jN5vKQhcFxa3+XaLmjfuAMAoNBeCOyRKvx79uyN9BR9E/5gcpIdqwEqtc7o0KE9rszwPa8/XyiC0yEppV69e8GmcQGdzDsDTvYYXNX+D8C1wX+jkiZr4ZA/X9OhIy43bAhnhUeWsSfObPTd74rrKaNhU+nvxono3asHhBw7lC7jVs73LqKbt8Ii15G063A12vWI/T7xeDxVGmAb5nr6n//5H+zevRuXL1+Gy+XCf//7X/Tv319dn5aWhvnz5+PMmTOglGLVqlW49dZbjRJHRR3kyIqCUkFXR2FPCR2jIJTCf9BCSfyynjyCb2rTCg/FjE2+H1RqCCj/XLoiO21TQCULRJQ3i00er+wO4mXT+mhRBcau2BPWreSRXV9uIXR86fnNh7F67+/qd14emQl+U1sqUFuG77OfS4HyDlBPqVo/kTSuJ6WQLMTfpFaguHWsHOAJrEyOiVCZTtoqbU22U/RxitC9nqguRhEYv1AsirCuJ03WE3hvQB2FNitKdT2p6bEENosFVt0z7fssEAq7okES7HoyTFG0atUKTzzxBO69914MHz4cQ4YMQY8ePTBx4kQcPHgQTZs2xezZs/Hwww9j0KBBoJTi/vvvN0ocFV/Wk6+OgnJ2UE2MQt1V0hYAACAASURBVBQI3A4vfj9U5BdU8584COC0L7IofbOUUszb+itOXNDXOZR7fQFhCgqHVxMYo76GgFqLQqcoKJFSY+U2AaEKB0PhlR8ARWHsP1OK3ScvoaDUGXIfj9zbxiOEfjF8eeg8vtMEu5UailABbWL1ud/8YxR07wugu5/TzA+QLMFsRVGYa8KamFDufwsH6g3zMg21u+6tGjzTSdB91j+bMcmIwIw5ootR0IB1URXcaYLV1Ov1S4+FLoZh8yu4s0GEzcrBqp1ulQjqQJQXCVJUUyNJYhQAMHToUAwdOlS3TBuXGDhwIAYOHGikCAH44iTSS9dtaY7DDV+Cl3sPgE9RHPvhDNwOL1q2a4K0jBRQSqXKbI4Dx/kqLnUj3ihHNaVOHsu+PYGGaXZ0HOCLd/DE97LlwOlG8hREjasoikK+fXwHphSW0vdBT+YDmBfUoiCUYOOxdRjScRhSrPp8bcWS8MrtNTxy1pQ7jGXilRWVJ8zD4xFEuDUZWIL8IIQKaBNrRuhMS94hL1diSkliUagvQApKRHCWWjhHvVZRVCWDUXs/aCwrrRWhzVTSVmTzUdRSSIpIU3Dn3+tJO1dFgBIhsCjn097rrhLgyKdAr7/J6zTPrMernk9dpvEGWJXDXSqDFVKMwmbhAuflJiKoxQpepLBz8jprYmdrrLOV2YpF4bE0B+Ws8HLSbGh2jUUB+BSBmskGwGr3PcQcjV5RXCoow/Efz8Iju7Y8fi4uwe9lq+3rorMoNFlPnPamohQW526IVNL/QpD02MMX8/DC7tnYU5gTsM5LFEXh1cnnCpNmq7zsveEUBU90ykaxKEIpChrGolCw8JI1ljSV2VqFV1vdT4rrqYoWhe4FHKp2IkivJ2mbaCwK3zbBZrjTtoMJsGQpURv36eQ8tAbYeB9QLs1Ip0uP5fkgFoXvHEow2/PTAQCyRWGx6C0KACC8qghTks31ZFZ8dRTSpStpsaLcZtyeYoWg+TGVuSkUH6gVgNXm+7PFoigqL7tQWlypjq7dfi9gnvhGYZzfegISGKOgHMBpXzCKrJKiCNYuvVTuJeTkA91JquvJT1H4y6mXWZD3CT2C9AhEPQahRH3YQ7XxIBqzOpSisLul+pGkcz0BvnhFrUNjUbirYlFo2tFo7o1IBXeA7/l08yI8oVyuVKsogsQhwrilQjYF9Mqha49czyQIgF3u1eT1wD8/lmpksyrzaMueg/pNd+BU2TF9jALAkcLLao+nVK5mXE91T1H41VEQefRNZUVh8yu4U2oqFB3gb1HoboQIioKI0rSpykszwKIgfhYFrx3hEE3Bnc/1xGkVhTqHsGxRBHlgyjxSOqBbcAWs88UoFEURhetJsShI8BGkSCi8IlGtI20AO6TrSSN3QMGdXXLV2T2KRZEkriedX7qWxik0weyqWRQ+5UI0L2Ndf6cgLTwAXxuPJz/5Bf9a80tY+aTPwdxLQdrhKF9D1VEoz5HiEhUEcOlyOxZvMIsi0PWkXFJG/YPIKdwJCxVwCY0gNO8GANh59JyqKOyQZWQWhbH4V2b7LArp5aoU3CkjWSL/QMpE7hYAVntwi0LXFPCb54BX2uvOLTqlGIRHVRR+FoWotyh4kaoPg9ai0AWzLUqFMgUvH0+k8jX5veDffm4b8j+/BABwi4HBZ15+2fP+FkWYoDhPlUyp4C83r+Jmk69Z0BVMhXA9af6OAQaFTXJL2b2yRZEsricigCr9vYK4nrz790O8bPL2HpoYhZr1RAhw/L9BfsggaF7AHt53f+qtCO3cFFqLQvp8tsSJsyWBgyBZGPUTF6R7rC4W4h9bAAlemc3L51IsC0EEV09RFIFZT9oYRt9mPaTlovS7W0HhET2wUh6f0j+jsvsE6dBeHl75PZRmlxPHWYzCWPzbjCuWBJFfrvYUq5TGJv/AoqDvJ2OlgNWmjVFoDq4d/e6YBZSe0p3be1L67vHoR+objq7Fh4dX6lxPCopVobUotKMSTlYU3xwphsejZBP5YhRa182x/edw+bh0juAWhRKYVoLZcowiWHrsz+8Cu+ZrAtPBXQ3+bjatFRHSohC1isLvBSMPv+xeSf5kmeEORFTdn8EUxYW7xqJSkwhiSlRFYfFZFD+/BXwwEDi4OvL+oRRFiCrtYDEKp1eEiw/huvN3PQVUX+uzDHW7hnI9BVgUPCzp0mBG+hv43b+aQVfnxp3k40n/WSmFW3DDSgV4qBVe+Z3E8x41RtGk+efyxsyiMBSOkyZBVC0K+eEUNa4nwPeyUv8nikVB9TEK7cGDuZ5EzShFXu+R01cVi+KLk5/j8xOfQdS4bxQXmVZR+CwKOdCusSguO3hYqBR3UILZlMrdbWW8HgFeuQLdFVRRSC97xTpQ5PMEi1HkfQTsf1e1CgQa3NXgs0qk/3WKIlSMQtRaaX4Pmry/ElhMGouCCmolsr/rifI8aGUlSFlZkB1NhDaYrcQoyuT6mcvHI++vsai9vO+zoLMiwscoXLwYuu5Hc68Em+FOpAKs8m8QGPsKoSiCWhRyMgYfPj3WrmZOSs+6DRQewQUr5eEhVrhF6XkXvB7wguJ60veHShR1TlEAADhNjELu1EjgsygAn2IIcD1RfTDbEixGob0BNS9kIqsV3imP6nnlJeqCS3BpRuWqEwIurwB64RcQKoCjFlworECFoLForNK56qVYYadSnyCB+NaLeRtUZeV1C+Dd8gg/SN2DGsRWLAq/l7wO3gF4HRCorybi1dxFeOa7KbrNfHEO2fUkRmFRaF3J/haFvI9S/JQ03WOJCJELblFQl3QPUWfoehZToLyIrZr0WGXkG82MhpoXMK/5HKrgTrEoLPZLePTbIThbcQZuXoQrSEytoGMnlM2Zo34PalEQHjZVUfjddxwJXkchP0d5Z85J+wm8qiioR+96AqU615NNVhCq64kCHlnx8LCh0ist9/JeX4zCrzV5oqibigJUHZ2rridFq8utspVRrSj4WRQUsGimK5QsFMW3LN94led9p1JGGgCo/OcWHNLNoLxEnYITbsHpS4/lfD8MrTgDeugd2EpPAZTD56t+wbunr1bOrioKgVBYRElRKBYFAAif/BU4+RUEXgQRKQSPtH0wRaHGKIg+RhF0hOZ1ALxDdT0JlEf+pcPIv5Sv20xRhi7ZDRZVMJv47DRyyW8iG3l/xV+cNMFsIoRWFLKCoM5QvneTEKzgTnmhhatmVtBY1IKm0j9UwZ1SR2FNKUYFX4rfy09LridvoOuJut1wrlmjfg8WoyBUUNt2BGbbkeATF8kDQY9TbuYoiODSpTYdwdNjg1gUos+iEGQXlgAbKuRNed4rxyhEpPgV6SUKQwvuTAvna9lN5E6NxMkDsMImB6qDWRQWyMFsm1+rcc4CKxV9ikJrZmvSUKlcREVcclBbeRGrFoXsUuKoGksRVJPW6et4qxyPUPUXdHsF2KgU4OWJRlFQG+AqUV1OitES3PWkr4lQrYFgwWw/i0KkXrgEAS6/tFvlGJTKE6/oWhIEVxSi9jrLfa0/KKUai0LjkqvlSNdAQsYoVIvCVTsUBcdpXE9K4WCYFi/q7prBiygEz3rSprAqFgVnkY7t5B1w8aKc+hokyUTbf8cv64lSCpHysCqKIsBSpcEtCtkCsAguEEpBRQEWmx1ISQG8Xl2IggKAJn5iVdZpYhSC3MaHhxVyiygIglRHwVl4n0XBXE8JgIPPouCUGIX0cr18VvIDE0JgsRAQTTA7Te4xbvGbAJ0oHVCVm/jyCd9KzYuTyI32RJc++8nFO+ESXL70WE6aSa9nqheiEtQTPaovUz2ek0KZ48TlFQCxUpZVM60rsQHeCng9sk9fNmeVrCdCRXx15nUUVOarikpVFDxBC0cJmv+0J/Bv6HUAggtEVhSECnDwTjgFh24zbWqtmycRg9nU6QQpl/5mHHHrr1nzwlAURVK4nhQ3WogYBZEtCeIyuesJPtcTFItC8eFH4XoStAFszctYCNXryU9RlLkrpbicPChRUBWs5laSXE8aZQICCgobp1gU+vuK47QxCs3vIw+4bKJTStHlBcBmBWe3B7TwAAUsTjcEm2JByIvle3xqk6vRWVaoPLWhzKucziO5njgvcz0lFqqJUcgWheKCkkf5VBRgtVC14I5QINUi7eOvKBSXkmpRuC77VvIa15NVujXcTinNUbEonIITAhFwvvKivJ2IVKsFM5qVqoqCEz0gXr2iEJ0EnF1a5vIKAJEUhUB9MQqB2ABvJbwuZb4DaZ2S9cQTN9xiJSr4i2pswqtxPb255SVkL302cD5w+bpsRDqOCC9cvBNO3qkbzWlTgD28GDmYXVEO0ZICi+iEhXhBvZqXo+blYVFmI0sG15N8DarridbyGIWFA3XLL33eryAtDILXZzFpEzv0AWyNG0pUFIW0bbnH96zp2t84HPJ2vufH3/WkKCDV9RQQo6Bh02NtohMuwYPCst9RJlaCS0kJyHqiACxOF1zpFnkfebnseqrPWdBSaZgJG8q9sluZ58GLBJzFixRKpUqKBLd4qZuKgtNkPamKQvrD++osvLBYqep6EkSCNFlBWGwhLArlZerRTD6jdT3JiuJU6Q4AUoCXF3nVb786b7u8nfS9sd0CopjjggeCJ4iisHKgIg+vVwBHpAdCZ1FQK+DxWRQcbwWoT1F45BGMV3SrcigWhZsX0cIlWVi0Ut/AUIm9pFBJPkp5OAUHKCg8mhoNbVGhWyB+dRRBfMleL4glFRbqBUcFULfT90Ar21tS1L47SdHCQ74uESm67wpqjMJVzY6sRhOs4E65/6OYC1zwOlX7UFuZrQtgBym4UywKnaLgAxWFv+tJa42K8gDGF6MIZ1FoKsjl67MJTlxwXoLH48RlvgxI8VkUasNqUFicbjjrSe8Lq9LYUlTeORQpSgdnWFGqup68GtcTAc9xOoWZCOqkoqCcz6KgfopCzWgSBVg44qvMphSpsuvJarUgB9uR4/5GWhegKHxpjJ69OeCPHpXOZZPOZRGU9FMCl6a1OCf/HMQqra9vs6i+XU70QHTrRzmCUz6f6IbbKwDEAQqLFJdQtvGzKCzUCo5Y1WD27t+lwLtbI4d/Cw8A+tRMkVfdI4Mrj6CFwINAUNuCaNuDaKvL3f4WRbAYhZcH4VJgIR6p+aLX5XNbKNvbM6SHjFJdf55ai+JGC5n1pASza4lFwWmynpQYWxRTdxLBBd6ixAg1iiJUwR3VK4pKb3CLgjjkv5vOokBwi4ILHqOwcNQXF5OfD+osAspPSfuJLpS6y2AjgJcTwaWkAl4elGqnNpBcT8506V1jUdJ71X6QIlLld0i3Rg6Uyc87EaSsJ47zwg4KnuMCXLxGUycVRTiLwipbDZTwsHCi6noSCZAm32gWG4c1Ze/gJ363tG0Yi8Lx7gpUvPa6FBy3yim4ssnsEQicmqCycj8Rm/SQpNlsavodJ3ohuDXZQISistyCs+dTAcEFt1eUXE/WJhA16bEC1ccoAMDK29VgdqVs7rtF30uI92vhAQBEO3Of5oGcWHEQwytKQDm3+uJ3apSOzqLgxcjpsV4vREsarNQDDkR69yiBUOUlIU9oYw0y73GtRH5JiaHqKGqb68mqKbhTXE9RWBQnfvXglTfagRc4qVKdUtBz5/yUQ2DBXTBF4dQMUIqKpG4E2hn3tOmxVORBlRe+YlEQHrTslGZ7n0VBlPvReVFNPbcKTpR5ymEhFF6OyDEKeZAnn5dQvUVBlXoo4rMo0uTr+2u7QpR45HePoHc9eTkLHF6mKBKANkaRKi/xsyiIACsnqhaFSClSZSUiQIBH9MArF5kRziIpAWWE4C4DbFIZPzwVIJcvgWqCa3ZqASD1fHKHsSgEiw0W+YXOiV5dPJD3isg52giL/tMeoseJjPRicKQSsDbWuZ4EYsPREzZ8vHCnuqw+GsEtuEEohShfwyW3L6XXv44CAEipxqLg9TdpE1EAZ9Wa/aEUBQHvdaOeW0npDVJBy3tBLGmwKIoCHKC4spQXqNzGw0oDUxxrJcRfUfi7nmpZ1pOFAxSLQrkXorAoTp8gOHs2HWUVNkl5nj8PcfNnaFQeyrpQziedy6G1ZDUWxTtfHfbJJcNR6oulFe1D+sH3kCKKqkWReiEf9OfFoHKrGCt8v4nygofgUH8rm+hCuacCNhHwQACXmiKlxxKCPr8dQCNnOaizEvasBvA2lH5nziP/rqrriaCLkvbKWeD0KIrJiwau3/D/tb8IO6XgwaGS93MFG0zdVBScUkfBgcojCCIrDr1FIaj1FIRQpMlmsUuOBTipHDzm7LKi0FgUDa6QTmUhEC9fhqjppplCbQDHSxYFr7Uo5JGHfOOLnB02MbhF8f+3d+ZxVpRXwn5qu1svNN10gyAqoAbFEeKOooj5BJFGiXFpjaIyickMxl9wokkmJv4+nTAOMpPIF5PPcUyMio5LokgmKlE/NyAoLQoqCCJN0wu9L3et7X2/P6ru0nTTqGNLO9Tz171VdavOPbfue+qc95zzWqZDb1rHsjTaG7pQlTSIJEIr6zOZ7QqNe/44jdaGvJdTqY8h42Zy7ixA0s57DLk24AX56H1CT/s8zYwQbm5CEejjJe0beip+/Dnu/bdeFCEHznqyLFw1jCpNUIRnwDP+tXOhJ99QCInAHSDnvS+WI+gk1q8StxDhCpp2tOMMkIM/5MhsBtfAhkIUpMf2SyoYJhSu9YBWkB6bCz11cqB+T6mE993iKQ2kg0x6/69opjBE5CLNHuSeV3D8hy9Vy6bH5g1F4RxFotO/9/cNPWU9CrMLBUnEcXMehWrFve/jh5H1go6uOY/CThUYiqxH4RmKbHps2MpwVNsexna3EG14kaq7zuekBf7K4P6Ef9ZQSKnQxQxcJQKKmgu/ua7NYdY2/nZcgpD0Qk9JO/Aohhzp11FEtZKcRyF8VWRrJISQqFj5OgqRn6NI+mmo3cLLbrK1EI6qkTb9wdLsgWLfUBgS0dmFm8zfxOUUc1goiRLeycL/qsltV3LBJ+88jqpjiOwchYVjFbTjyDgkTc8gNO3q5qvOkXSVnILQSrBFPnWucL4id32tirSTxnIEipKtrM7/GbMeRSiRNw6ipwcpJS9va8HJ9M1gGeH2Hdj261E4LvquBkYmJMUZ2Sfrqb2pl8adHd4chRpFw0JBgqJBym+ENkDoCQbIUNmH3V0p2tUSOlP7L/rau7OTxm1ttO3u3u8xQ0YuPVYH1NyiTFkKQ06FCwK1XrSA5GP/+YWIeEAKPTtlgMls6R4w8ymV9H7HRMrwitv8yXujIMXaEQ5y6yPInc9QofSgKgV1FAXeecryVqQTDQ3Y2UQMNdu2xzMUubCl/3QeEiJnKJSs3P4+vWAuzPHbi0gnbyhGdr+H3rENTUBGsVGMENK0CPnHRmwLNe2t8lhaUUTlv5yPofjZkf6p49okmpTz6B1xFqgaRf5aqdKxMUQSVYEYim8oAo9i6PE9iohWnPcoUFCQOfdUuAoqdq4y25WSIt/biPsV0EmZwJU2thamNSNoavf/CGYvFI/xLqVLRGdnrnMswBQ5ltWHpTBiu/uIpfpzHQomSImr6uh+aEhxrT41S7blkra945vq4hQRIVk0BUsvwpUFhkL0NxQj1QoyTpqM4+YMRWHsN9s+oajQUHR3s7mhh0W/38jGnQ19zjdinwyMwj9soUehZLrQOryBuCze16O4veZxll7/R7AshBZFxfImsxUdUv4yqgWT2VBgKA6QItvhG4iBWjt4n5e01XuhEbFvb6kvguwkKSqo+n6zngpfi3gcq7YWc13/BagODgUN9zQFae6THgt9OxYMQDrl/ffiKc3PePM8qbBVEAKVIjd4u2Y3YV3LebOZfTzZ8bt3I577M2UZf1DNNY5WPGfBTXhFn9kMPlfkQk9qrtlf1lAUeNd23lvKpswabpKrX/spuivJSNtLj7UtQv48RdgxwfbGh6JYBOPwUkIh3+D4HoWtjPBOa1SBolLsl25rOIRcT8YYYCkKqcCj+ALIehRqEULxy+2lt8RpNvTkugqGksKxXISQCCEZFdaRSt5QAGTcXiw1TL2tUZZtjJfpgehIpGKg6gKZSOD25p+mhKJRpoKqD9zkbUz9W2jSwVEMDP+cqrBz7TfA8yhSfk1E027vprZCozHDRbjSyB1nu/0Lc0YoIwF/IjvnUeQHW0tYOK6gNJX/nrKnh7oO7+bs7O7oe75BDEXWo5hYlORrvfdROsr7U4yMi1xldp8qWjOFq0ZRMb3wgGJA0qsvybXv0POhJzhw0V1Xyp9k309YybXdXFaYnTkYoads36qsoRh4MhvyhsLd6w26Tv2eL0jIA5Cta3FE3xYeVhJKxnmvexv282GPVNL77yXSOqoUOY8ibBeGnhxPR0Ciu5WIoeY8isKEjLTtUhL37t/R0p/n6+NRSNoz9Wxuf4mmrm2AbyhyHkW22Z9vKPYJPcmm9dDWf90LVUAaK5ceG7Y9PURsC9XtO7hr/oIUUipIwFY9Q2H7hqLI8FuU4BKW3mejgK0oJAJDMfR4sUmV4/9wK0L1Q09SQVFA9W8mV2qEVO9G+z9PXsnL7y1nTFhBV9L02PkB3hEJbC3E5pRGuSrIpE2k2Uu8fjeoIRTdG8zslvbcZ7LptEVGhpND03Pbs5PZVY2b0NwMrqpj+wV3qmvjFKTHWqZDxvco9jb6tRDGGOxYFKfAo2hLV/b7/mHhPZH3dCUIZ/yW5AWhJ8v1esuUp/Pf0+3uZk+Xn/6abO1zvhFuX0ORtlJsqG1g8ZLV7Ln3TZCSKSWeLksmeAsPjSzwKDqa80Y00ZVEqDE0TK8VhKJDOmso+noUes6j2L+hcISgJ+Mbiv2s1Gem8oOAlcm/bu7NDLoM7L5IK47seP8TH58j2wlX8Q3FvgV3BT2eskbDbfaa0Dl76hkWZH8LR6Boaj5EZqegwu9NNoCh+P7jm7jlqXeRUpBM+YYiqaHggu9RhGzQFB2FbP2Ad9zh0TSqoqD6hqKwfidluWj+fVnhZohefwT6VP+/ruSDvGt2vYSZ8UJAYeGipmxOuOg/UHu8hyFpJ3ir6U3cusb8V3Us5Ed/zN+XBeiKJIPnUWDZGJZv7BwTlQwimQ9/6lrWxQFQcx6FbVSBqlFqZDvGukTxdBGVYCtqn+zBL4JD0lCgSFQXom078oYCz1AoBYYijDdQ1jQ2cPqHKzkx0oXR+jb2+1typzKERUYLcURlM0p5K61NLSh2EvH8BoSt5AyF055/Cs/2fPre6Mv4VvESDtMO98QqKM3R3TiOqhPRvJtdFU4fjyLRYyKkgqEL2lsthJDYoSrSJSV9PIqW1Jh+X191vP0977ew499tXrt5BL3vR3L7LdfCtAUTeppxFdg7UiXVsZc9nX6RXqavoSiSAl0KRsgIlbIIEhpqU5wje23oMSl3YVKRZ2RiXykHBcoT+TmK+g/zf7jOjhRCi6EqFgoaUtHyle77Tmb76hisMWB3Ot+YbX+DftZQKCEt71m4gld3tvNBy4ErirPIPf8PueV+ZGHB5SchG0ohBKqx3zoKKPAomj2PQrS0fqZsKGf3bkTyc3wqzf4GWR0XpsdW+Osu7GMopJS8ur2N13e0g2uSTHkPLcmUjoqbq+6OOKCrGpqqeSFSP4QzPpqmI2mh6dnWMwWdmjN5ozFaiVN3ygo6TrrGu643kw1AR7qTkarfMdYyUd7bidGRQvXjvE17Y7yw7iU613veg0Alk0l696L/O2WOOpudR3hzjeGow0ilCNHejjBNND/0FHEyaKqDXZefA9ONvKGQioKtep6+ZVTiolES8nQZVW1CinetsISQNpHZEy44wA/y+TKkhmL16tVceOGFzJ49m5UrV/bbv3XrVi655BLmzJnDT37yExzni3H7pSJR/Wwm6RsKrzBG6RN6CuEN7kpoLBPTLURCpaxfl2TdvaNQ/F7xxa6Lo4Y4ffReKOsk3uz9GaSjIlJ2zlAUrk4mVA1H0Zgc9v5AR2qTAFB9Q6EgMJxObC1CVC9wu01JyFCIxQRN/qTrkWMSuI4k3p3BMkbTVX4YjjAI+dXdDYnx/b7/EeEJHFY0FsMO89F7raTaFbo3lqI7ElVRsFyLpGUxpacec1QpqWKdnr1N/M3j93FMRz0R+ofMRrguv7UvZ7W9CCPl6bSr3RuIqmzBpGI/zbAsgn7ECCqSGm/WtbKlsZv1f96eO09Hl+nrwgbpeRQym1qZHUB1z6Mw/Ew1x3VwsgsjFdSL2K6gLWFB8256f/MAia6C1ioFZA3Fa41dxBPe9TtTFhJ30AnwfsT9MFDvrtwmGd+D2LlqUK9HxhtADWGqJaBqfNjUybqdBR5ogSHI9n3KehQATmP+afeTIDMZWubMpfdflnnnEi5O41pkS+0BP7tqx9O82b1+gJPmPQoA6913SD72GFhJ3EgFRCv6GYq2uElXymZvb4aOjm4sO+9ReKEnP6Sa8BIpNEXHcR2E2QvCZXJ5gumTOxHkQ0+q4s1ZV8Tzv/WoKh2hxciUHAtS5uYjAbrS7ZT5fZOaOpoQW7Z63ZtD3ldq7SrnLONrlPhZg8LViGTXrc6mNYfK+GjsAgC0YpeJyihU28ZNpYj48xkh3yOwC5IltFC2RxyAiq2WeceEqkiIMCX+HEWZXpAxCahqOaXhEYP+Tp83Q2YoWlpa+MUvfsGjjz7KM888w+OPP85HH/VdvOSWW27hZz/7GS+88AJSSp4oaAM8pCgCVUgkGlIxUISVNxR4T2yayBAS3UhczNDhjHMF0hjJx/U6lhmipGUsYVcwOvUxiqJRFIqiaAK10xv0Sq6YBg55j8KvQ1DMNNpRI+iqzD/BH6FP9PZlV7CTgp5d77OryaLTOYF2qxQpFdyUTkiTHHWETWujd+NOGu3deN0dKaQaQtneiyMNQv5TVnNyHBWRtty1ZMyidUeS5dNX0NbkDd5GSMP5IMRj/7uHy163aUnuKcGRWAAAFa9JREFU5dVHjuei89cw+isRjo4ezoh3tnHu2mf52w/+i6+U9ne5J7ghxuHdvKPixXSkTXq7vKe60pTNcSO7+MifHI9MHUNJj2RHWzcP/PovfLBhD3Pnek+TXT3egK9howhIFJ9Ed6qMdf/1Iam4/5SYCz15+lq59K/c/d1V7NnwOj+48Pe889ybiM2PsuDXa7nzT+/T8fTrbN42luan1wCws6OXh997iLdbXkFIl0zSwhQuCw77EF2C0/gWbYk4sRHPklSeI/MJqmClFDlDIbvzTSHlBw/Bnv/Hm2+9hnBdxPvvIbZsQRaG6+J7oHgcKCoZodPT08mNj22itddPn0ylwE/Nzoee8hPDTv0eb1TbtgrqDzy5bW7YgIzHSa/5C1JK/u2vPyfz4X9ibn8Sx7Jpq+/us8pgloyT4V/fvIsnmh/rXwOTnaOws208VLp+9EOQLv/3r/X0hMsQvX3DZNv25r21t7c15V4nUzqaInLpo2VEsYSFaUte3LIHtekdlLX/ztGtr/Kdia/419eRmEQNhaihcXS8BUfXaYmUEh5T4X3v6FG4poNQ8sWtVibvrZWpBt21G1BLwiiKgilLcUWYYrWESqfKu4wJRaS9QjvXoa3i62wrvoNjmrx7Wyt2GaOOQNE0MskuQtm5Dv/Bjfbd0OXpIVuna2oKilJgKIxKkjLCCN+jKNUKU+tlvoL/C0SRB0pC/4w8/fTTvPXWWyxduhSAe++9FyklN954IwCNjY1ce+21vPjiiwBs3LiRFStW8NBDDx3w3KZp8t5773HCCScQDn+6drs97Xt5+TerccNHUdn+FK1VV6M5vWzbmqatqYeraiI8+PsUpx+xnSnHd1OnVBNyuihOvM2e2MVseOljAE4Yv5PJI9+mSHHYPf6nxGik2K3z3M1EE3LUBOhsRtoOTrKYTNlE0lUnek8h/mRcb3MLuz7qZfK0MqLmRtBLEWVnUdn4Hzz77qnYMsLxJ42l4aMWph2f4P1dlch4K6ed2M6f13qewsLZu3hozQQmHl/JEUdXEG14k3d3QdrS6Ep6A+ppE3fy5see13LCMR1s+3gk581U+ai5kl3bWpl25pFsWlvPvGN2ENJc7GJJkep30SWCVEfj+oZOKqCPAE30TSNNa5WEKEYg6B55KqmuVv663jNWYytTnHVKL6m4SVT36lfMjjSZYoVN741GVQSLv/Yidz77dSaOMxk77atU2q/ToZyG0MM07upix5YWJh6e5JS/SdDa7lA1SidjChIpePmvYyhsDVpZGue8yd5cQZs7nlc2jUNKiIZsZk2twyw/FcdJQmg0qrQQRjm600Z576u0ll9KuVNLKtGJEAJN1fxYxb7PVKLPNkVRKC7ys/MFJFMSKaG0RAEUHFdiJSQR3wvKKA6m4qArIYqLFUwT4glBNKpQXKTSHXcRRhXSGENJ4waUbG1LaRFuWMfoSXn9roRAhAzUmEXY6UYqGnFjXJ8qZKmEsYqOR8/UoTld6MkMenbxrPISpO5QWuQ9uHSJo7DDRxJN7kQx9yAL1voVUuSq9kNqCEXJrumi4hQfT1TtItxTh1oSxt7djUQSLu4mbowhLBNowiShj/X15enQdr2bKm0arH27ElUVFMdc/teZ7ahxFemv9tKp+M33VJ0yZ2++QR/QoVeCoiOkYIRMAgoaFWRUCRLc8GGkSyYDUJZaj+ZkSBUdjWm7aKkPGTsqxITxKVxXYH/QjmqoRI6rJKEeSasx0/tc3Soq3Fcwe8sIl3YjK44CPUyjMQ8zPJ7SxEeMarkXJ1mMLDoKadm48TjKmEp0qaBGFFS9DaW7wZuLOmwKZHpQOuvpap1I2WEN7DriDi95Q1GpSD1DOGoQ/fhFkqEqYlXjPF3s/YC9Aqbc9Cyfhc86dg6ZobjvvvtIpVIsWbIEgCeffJLNmzdz5513ArBp0yaWLVvGY495a+nu3r2bG264gRdeeOGA585+2c9C/cP38eLbpw64r6QswvfPWcldf7oIWwxstTVVMLakmT094z7T9fclFnFIZfqnsMaMJIRKSSVdIjGDjB8emVy+lXnHvcG/rv02ADWLT+f5/9xCd0ff9g5jjyqjdGSUbZuamXP5Cbzx3A5SCZN535zKnx7JZ2tMGFHH1Ivm8szDfRcc+jxQFEnVuBG0NAwesz+n+itMPK6S1Q+/Q8fegfPDy0bF6G4fuIWFEdKIRA3iPRnKq4robO3rAVRVaUw/q5iXXs6Q6PkEK60FHDTGTRhJ464DV3F/nigKXPKtUygpi/TbZ1gtKNLGCh++389H7UbSxuczHpT2vE7viLMHPaai5xm6jpv937rOpzUUQ7ZwkRAi1+MEvImrwvcH2v9J+Cwexckn/zv6PXdQFhmB0MsQio1lCCDCyFCCxpJq5kwsxWhrpZgUuqph6eORIk1YTRMpi6KFxqG0m0jFJRMeTXeyAU2NoUjAzGDppdijJhDqrIOIC5qKEY+jaBZqcTlu0sLARNUtRsfCtHVFvadXQpg4dIU1VJFgfKlFPAF6SCPZYxBVLMrGT2LE2K+xaIZNjxWnuOhtrrisg+5eE/2IabhJE9vsoZQ6DEPlq2eUU6ytp+abLlJCSWQjf3u9gm0rIASxohiasZ6rrg/jhsvRFaC3G3vURHRVJRxOgevS29GGFgtjZEycjMA2ihGOJJ2OM6m8DFdYJFyvIErDxHAyxGKghjL09koEBjqlWLoBoQwxMwOKoEeJUDRiJzgfMbe6h1RCwTEU0qFibBGhNJMkJpsoHRmiNxHGtDV01ZvsVKRCWFeRuomiSXoSBiPLekmnipBaBbri4jidlBU302GqXHllhPZkCSGSxKQBUpJ0etCNIiKqIBWqJJVsQ9FHUCodQGI5Di7e6zwKKhoCt892iYIjdVRFouGgAoahIzBQEbjCJu3f41Ghoikqpmv6n9NIp9JEYzGihsRQwLZN7DBIo9SLqVs2ipXPdpHFMaSmoyRTSKnjajFUYaL1C5VJFJlCKlGyXpAsiqI5DtK00dDRtBiOtHFFEkjj6MXoTv9nyJAaJpVOoYfz1f8aCgpJ0iKKDMXQojqq64BhEBECZAzbAZnpRMX1V37wdKj7vbZtR1IcC1Fa2kV3Fzhuyqt3MkJ4ZRKCWCiM5ZpYUsUMVaChEpYZYtJCIknbJmo4hhSQdpP0JOKUFMcIqwpmaRmW40JvGgWViBUnpIUJh8MUR3VEaBOKaZGOGggDRCKJ6RqUKXFihoGr1JEaMxoRHUm0tROlt5OeTBKpaYwaO4ldvZsociRRO4kYWUZKcYm1d2I7aUTYICrDJFzIVE5CNzQq4nGUZBdpFIqOnITatgfNiGArSRTWkVRKQLpENIGTbEeRAl3TKRlVwaivfY+JseJPNe5l+awP2UNmKMaMGcPGjRtz79va2qiqquqzv60tHztvb2/vs38oGTtjHieffPIXcq1PwpEDbKutreWEQWSsmDp08nwaamtrmTKMdLk/amtrmTbM5aytrR1W9+X+OOTlnNJ/U//cwk9GbW0tx38JdDlkk9lnnnkm69evp7Ozk3Q6zZo1azjnnHNy+8eNG0c4HKa21su0WLVqVZ/9AQEBAQHDgyEzFKNHj2bJkiUsXLiQBQsWUF1dzYknnsi3v/1ttmzx6hCWL1/OP//zP3PBBReQSqVYuHDhUIkTEBAQEPAZGbLQE8D8+fOZP39+n233339/7vXkyZN56qmnhlKEgICAgID/JodmZXZAQEBAwCcmMBQBAQEBAYMSGIqAgICAgEEZ0jmKoSJbI2hZn6IPzz6Y5vAvvPoyyAiBnJ8nXwYZIZDz8+SLlDE7Zn7aOushq8weSuLxONu3bz/wgQEBAQEB/Tj22GMpKSn5xMd/KQ2FEIJkMolhGJ+6mjsgICDgUEVKiW3bFBUVoaqffObhS2koAgICAgK+OILJ7ICAgICAQQkMRUBAQEDAoASGIiAgICBgUAJDERAQEBAwKIGhCAgICAgYlMBQBAQEBAQMSmAoAgICAgIG5ZAyFKtXr+bCCy9k9uzZrFy58mCL04drrrmGefPmcfHFF3PxxRfz7rvvDht5E4kE1dXVNDQ0ALBu3Trmz5/P7Nmz+cUvfpE7buvWrVxyySXMmTOHn/zkJziOc1Dl/PGPf8zs2bNzOv3LX/4yqPxDza9+9SvmzZvHvHnzWLZs2aCyHExdDiTncNMlwD333MOFF17IvHnz+N3vfjeoPAdTnwPJORz1OSjyEGHv3r1y1qxZsqurSyaTSTl//ny5Y8eOgy2WlFJKIYScMWOGtG07t224yPvOO+/I6upqOWXKFLlnzx6ZTqflzJkzZX19vbRtWy5atEi+8sorUkop582bJzdt2iSllPLHP/6xXLly5UGTU0opq6urZUtLS5/jBpN/KFm7dq284oorpGma0rIsuXDhQrl69ephp8uB5FyzZs2w0qWUUm7YsEHW1NRI27ZlOp2Ws2bNklu3bh12+hxIzp07dw47fR6IQ8ajWLduHWeccQZlZWXEYjHmzJnD888/f7DFAuDjjz8GYNGiRVx00UU88sgjw0beJ554gttvvz23nvnmzZs58sgjGT9+PLquM3/+fJ5//nkaGxvJZDJMmzYNgEsuueQLlXdfOdPpNE1NTfzjP/4j8+fPZ8WKFQgh9iv/UFNZWcmPfvQjQqEQhmEwadIk6urqhp0uB5KzqalpWOkS4LTTTuOhhx5C13U6OjpwXZfe3t5hp8+B5IxEIsNOnwfiS9k99rPQ2tpKZWVl7n1VVRWbN28+iBLl6e3tZfr06fz0pz/Ftm0WLlzI3Llzh4W8P//5z/u8H0iPLS0t/bZXVlbS0tJy0ORsb2/njDPO4Pbbb6ekpITvfOc7PPXUU8RisQHlH2qOOeaY3Ou6ujqee+45rr766mGny4HkXLlyJW+++eaw0WUWwzBYsWIFv/3tb7nggguG7b25r5yO4wyre/OTcMh4FEKIPg0EpZTDpqHgV7/6VZYtW0ZJSQnl5eVceumlrFixYljKuz89Djf9jh8/nnvvvZeqqiqi0SjXXHMNr7766kGXc8eOHSxatIhbb72V8ePHD1tdFso5ceLEYalLgJtuuon169fT3NxMXV3dsNVnoZzr168ftvrcH4eMoRgzZgxtbW25921tbbkwxcFm48aNrF+/PvdeSsm4ceOGpbz70+O+29vb2w+qvB9++CEvvPBC7r2UEl3XD+p9UFtby3XXXcc//MM/8PWvf33Y6nJfOYejLnfu3MnWrVsBiEajzJ49mw0bNgw7fQ4k55///Odhp88DccgYijPPPJP169fT2dlJOp1mzZo1nHPOOQdbLMBbX2PZsmWYpkkikeDpp5/m7rvvHpbyTp06lV27drF7925c1+VPf/oT55xzDuPGjSMcDlNbWwvAqlWrDqq8UkqWLl1KT08Ptm3z+OOPc/755+9X/qGmubmZxYsXs3z5cubNmwcMT10OJOdw0yVAQ0MDt912G5ZlYVkWL730EjU1NcNOnwPJeeqppw47fR6IQ2aOYvTo0SxZsoSFCxdi2zaXXnopJ5544sEWC4BZs2bx7rvvsmDBAoQQXHXVVZx88snDUt5wOMxdd93F9773PUzTZObMmVxwwQUALF++nNtuu41EIsGUKVNYuHDhQZNz8uTJ3HDDDVx55ZU4jsPs2bOprq4G2K/8Q8kDDzyAaZrcdddduW01NTXDTpf7k3M46RJg5syZbN68mQULFqBpGrNnz2bevHmUl5cPK30OJOeNN97IyJEjh5U+D0SwHkVAQEBAwKAcMqGngICAgIDPRmAoAgICAgIGJTAUAQEBAQGDEhiKgICAgIBBCQxFQEBAQMCgHDLpsQGHHg0NDZx//vkce+yxgFdVXlRUxMKFC7nwwguH/Pp//OMfWbFiBZMmTeKBBx4Y8usFBAwVgaEI+B9NJBJh1apVufeNjY1cd911aJrGnDlzhvTazzzzDEuWLOHiiy8e0usEBAw1Qegp4JBi3Lhx3HTTTbkn/F27dnH99ddz+eWXM2vWLP7u7/4O0zR59tlnqampyX2uqamJGTNmYFlWn/PF43F+8IMfUF1dzfz581m2bBmO47B06VK2bNnCPffcw4MPPtjnM7fddluftQZWrVrF4sWLAXj55Ze57LLLWLBgATU1NWzatAnw2k78/d//PVdccQXnnXce11xzDR0dHQCcd955fP/732fu3Lm5dQ0CAj5XvuC25gEBXxh79uyR06ZN67d9+/btcurUqVJKKe+66y75zDPPSCmltCxLVldXy+eff16apimnT58ut2/fLqWU8pe//KVcvnx5v3Pdeuut8s4775RCCGmaply0aJG87777pJRSXn311fK5557r95kPPvhAnnXWWbn1R6666ir52muvyV27dsnq6mrZ2dmZk/Oss86SyWRSPvjgg7nzCiHkt771LfnAAw9IKaWcNWuW/NWvfvXf0lVAwGAEoaeAQw5FUYhEIgDccsstrF27lvvvv5+6ujpaW1tJpVKEQiEuu+wynnzySX74wx/y9NNP8/DDD/c712uvvcZjjz2GoiiEQiFqamr4/e9/zw033LDf6x933HEcfvjhvPLKK0yYMIHW1lZmzJjBo48+SmtrK9ddd10fWevr67n22mvZuHEjv/vd76irq2PHjh1MnTo1d9wpp5zy+SkoIGAfAkMRcMixZcuW3AT3zTffjOu6zJ07l3PPPZfm5mak39WmpqaGSy+9lNNOO41jjjmG8ePH9zvXvq2hhRCfaJnNb37zm/zhD3/gqKOO4vLLL8+1w54+fTq//OUvc8c1NzdTVVXF3XffzebNm/nGN77B6aefjuM4OTkBYrHYZ9ZHQMCBCOYoAg4pdu3axa9//WsWLVoEwBtvvMHixYtzWVDvvvsurusCcNhhhzFt2jSWLl3KlVdeOeD5ZsyYwSOPPIKUEsuyeOKJJzjzzDMPKMecOXPYunUrL7zwAt/4xjcAmD59OmvXrmXnzp0AvPrqq1x00UVkMhneeOMNrr32WhYsWEBFRQXr1q3LyRkQMNQEHkXA/2gymUwu60hVVcLhMDfffDPnnnsuAEuWLGHx4sXEYjGKi4s59dRTqa+vz33+kksu4c4772TmzJkDnv+2227jn/7pn5g/fz62bXP22Wfz3e9+94ByhUIh5syZQ3t7O+Xl5QAcffTR3HHHHdx88825NQp+85vfUFRUxOLFi1m2bBn33HMPhmFw0kkn9ZEzIGAoCbrHBgTsByEEd9xxB2PHjh10zuGzkEqluPrqq/nZz36WW8s5IGC4EoSeAgIGIJFIcPrpp9Pc3Py5r13w+uuvc+6553L22WcHRiLgS0HgUQQEBAQEDErgUQQEBAQEDEpgKAICAgICBiUwFAEBAQEBgxIYioCAgICAQQkMRUBAQEDAoASGIiAgICBgUP4/IED83Ah5nNoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# version 2 - Matplotlib plot with ColorBrewer Paired color palette\n",
    "with sns.color_palette(\"Paired\"):\n",
    "    fig, ax = plt.subplots()\n",
    "    for year in df.YEAR.unique():\n",
    "        dfx = df[df.YEAR == year]\n",
    "        ax.plot(dfx.DAY_OF_YEAR, dfx.PRCP, label=year)\n",
    "    ax.set_xlabel('Day of year')\n",
    "    ax.set_ylabel('Precipitation (inches)')\n",
    "    ax.legend()\n",
    "    fig.savefig('precipitation_by_year_v2.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>Matplotlib requires us to plot each year separately. A for loop works well for this.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C3. Create a set of subplots with a grid of 2 columns and enough rows to have one subplot per year (e.g., to show 10 years, your set of subplots would be 5 by 2). Plot precipitation versus day of year with each year as a separate subplot. Save your figure as a PDF file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 734.4x1080 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn relplot with facets\n",
    "sns.relplot(x='DAY_OF_YEAR', y='PRCP', col='YEAR', col_wrap=2, color='k',\n",
    "            height=3, aspect=1.7, linewidth=1.5, kind='line', data=df)\n",
    "plt.savefig('precipitation_by_year_grid_v1.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>The easiest way is with Seaborn facets.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matplotlib subplots\n",
    "fig, ax = plt.subplots(5, 2, figsize=(10, 15))\n",
    "i = 0\n",
    "for year in range(df.YEAR.min(), df.YEAR.max()+1):\n",
    "    row, col = int(np.floor(i/2)), i % 2\n",
    "    axis = ax[row][col]\n",
    "    sns.lineplot(x='DAY_OF_YEAR', y='PRCP', color='k', ax=axis, data=df[df.YEAR == year])\n",
    "    axis.axis([0, 366, 0, np.ceil(df.PRCP.max())])\n",
    "    axis.set_ylabel('%s precipitation (in.)' % year)\n",
    "    axis.set_xlabel(None)\n",
    "    i += 1\n",
    "ax[-1][0].set_xlabel('Day of year')\n",
    "ax[-1][1].set_xlabel('Day of year')\n",
    "fig.tight_layout()\n",
    "plt.savefig('precipitation_by_year_grid_v2.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>With matplotlib subplots, the trick is to figure out the axis array coordinates for each subplot.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matplotlib subplots (without 1-dimensional axis array)\n",
    "fig, ax = plt.subplots(10, 1, figsize=(10, 20))\n",
    "i = 0\n",
    "for year in range(df.YEAR.min(), df.YEAR.max()+1):\n",
    "    sns.lineplot(x='DAY_OF_YEAR', y='PRCP', color='k', ax=ax[i], data=df[df.YEAR == year])\n",
    "    ax[i].axis([0, 366, 0, np.ceil(df.PRCP.max())])\n",
    "    ax[i].set_ylabel('%s precipitation (in.)' % year)\n",
    "    ax[i].set_xlabel(None)\n",
    "    i += 1\n",
    "ax[-1].set_xlabel('Day of year')\n",
    "fig.tight_layout()\n",
    "plt.savefig('precipitation_by_year_grid_v3.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>The axis array is simpler if we have only one column of plots. This looks nice, actually, but it's not what we were going for.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer to Question D - Plot distributions of the precipitation data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "D1. Plot a histogram of precipitation values using the Matplotlib function `hist()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matplotlib histogram\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(df['PRCP'], bins=50)\n",
    "ax.set_xlabel('Daily precipitation (inches)')\n",
    "ax.set_ylabel('Day counts');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>This basic histogram shows that most days have zero precipitation, but not much else.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "D2. Plot a histogram with kernel density and rugplot with the Seaborn function `distplot()`. Play around with the settings to make a histogram that represents the data well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/luke/miniconda3/envs/python3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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PYvjw4SXmvfHGG0YJQ0RElkfvEcW4ceMQGRmJvLw8tGvXTpqv0+nQqlUrk4QjIiLz01sovv32W2RkZGDGjBlYtGjRv2+wsoKrq6tJwhERkfnpLRQODg5wcHDA+vXroVKpkJubK3VsP3z4EM7OziYLSURE5mPwhrstW7Zg4cKFUKvVUqHgVU9ERM8Pg4Xi+++/x+bNm9GyZUtT5CEiIgtj8KqnGjVqsEgQET3HDBaKbt26YdOmTUhJSanUndlZWVkYPHgwEhMTSyxbtWoVevfuDT8/P/j5+SEkJKRi6YmIyOgMnnpau3YtVCoV5s2bJ80rbx/FxYsXMWvWLNy6davU5VFRUfjqq6/Qtm3b8icmIiKTMlgoLl26VOnGt23bhjlz5uDjjz8udXlUVBSCg4ORlJSEjh07Ytq0abCxsan09oiI6MkzeOpJp9Phu+++w5gxYzBy5EisWrUKGo2mXI0vWLAAHTp0KHVZdnY2mjdvjqCgIOzcuRMPHz7E6tWrK5aeiIiMTiYKr3nVY+nSpYiJicHIkSOh0+mwdetWvPDCC5g5c2a5N/LSSy9h/fr1qFu3rt51rly5ghkzZmDXrl0G28vPz0dUVFS5t2/JrOyccTry3/6bLq3qQpPLsbSIyHi8vLwqdPbG4KmnEydOYMeOHVAqlQCAXr164ZVXXql8wn8kJycjLCwMAQEBAAqenGdlZTBOMRX9sJbo4pXr8KjvIU3XcneDm0sjMyayHBEREWjfvr25Y1gc7hf9uG/0i4iIgJeXV6X+yDZ46kkIIRUJALC2ti42XVm2trZYunQpEhISIIRASEgIXn755cdul4iIniyDhaJZs2ZYuHAh4uPjkZCQgEWLFsHT07PSG5wwYQIiIyPh4uKCefPm4b333kP//v0hhMCbb75Z6XaJiMg4DJ7rmTNnDubPn48RI0ZAp9OhW7du+PTTTyu0kdDQUOn1d999J7329fWFr69vhdoiIiLTMlgoHBwcsHjxYlNkISIiC2SwUJw5cwZr167FgwcPis3fvn270UIREZHlMFgoZs2ahTFjxqB+/fqmyENERBbGYKGoXr06xo4da4osRERkgQwWipdeegkhISHo3r17sfscateubdRgRERkGQwWivT0dHz11Vews7OT5slkMpw/f96owYiIyDIYLBRHjx7FyZMnUaNGDVPkISIiC2Pwhrvq1avDxcXFFFmIiMgCGTyi8PT0xKhRo9C7d29YW1tL83kXNRHR88FgocjLy0PDhg31PnyIiIiebQYLxaJFi0yRg4iILJTBPgoiInq+sVAQEVGZ9BaKkJAQAEBCQoLJwhARkeXRWyg2bNgAIQQCAwNNmYeIiCyM3s7shg0bwtvbGxqNBu3atZPmCyF4ZzYR0XNEb6H49ttvcefOHUyYMAFr1641ZSYiIrIgek89yeVy1K5dG9u2bQMAnD17FmFhYdBqtahTp47JAhIRkXkZvOrp/PnzGD58OA4fPow//vgDw4cPx5EjR0yRjYiILIDBG+5WrFiBjRs3onHjxgCAa9euISgoCH379jV6OCIiMj+DRxRqtVoqEgDQpEkTaLVao4YiIiLLYbBQ2NraIjIyUpqOjIws9mwKIiJ6thk89RQUFIRJkybBw8MDMpkMN27cwIoVK0yRjYiILIDBQtGhQwfs3bsXFy9ehE6ng7e3N6pVq2aKbEREZAEMFgoAcHZ2Rs+ePY2dhYiILBAHBSQiojKxUBARUZnKVShUKhUePHiAjIwM6ac8srKyMHjwYCQmJpZYFh0dDX9/f/j6+mLmzJnQaDQVS05ERCZhsFBs3rwZHTp0QJcuXeDj4yP915CLFy9i5MiReh+hGhQUhNmzZ+PgwYMQQkhDhRARkWUxWCjWrVuHzZs3Izo6GtHR0YiJiUF0dLTBhrdt24Y5c+bAzc2txLKkpCTk5eXB29sbAODv748DBw5UIj4RERmbwaueatSogZYtW1a44QULFuhdlpqaCldXV2na1dUVKSkpFd4GEREZn8FC0a1bN2zatAl9+vSBjY2NNN/Z2bnSG9XpdJDJZNJ04TMuKioqKqrSGSyFlZ0z4uLjpOlaVbVIuFm+PqDnQUREhLkjWCTuF/24b/Sr7HemwUKxdu1aqFQqzJs3T5onk8nKdfpJH3d3d9y9e1eavnfvXqmnqAzx8vIqVryeRhevXIdHfQ9pupa7G9xcGpkxkeWIiIhA+/btzR3D4nC/6Md9o19ERAS8vLwqVSwMFopLly5VKlRZ6tSpAxsbG+mXunv3bvTo0eOJb4eIiB6fwc5snU6H7777DmPGjMHIkSOxatWqSl/KOmHCBGmAwWXLlmHRokXo378/cnJyMHbs2Eq1SURExmXwiOLLL79ETEwMxo0bB51Oh61bt2Lx4sWYOXNmuTYQGhoqvf7uu++k182aNcP27dsrEZmIiEzJYKE4ceIEduzYAaVSCQDo1asXXnnlFaMHIyIiy2Dw1JMQQioSAGBtbV1smoiInm0GC0WzZs2wcOFCxMfHIyEhAYsWLYKnp6cpshERkQUwWCjmzJmDBw8eYMSIEXj11Vdx//59fPrpp6bIRkREFsBgH4WDgwMWL15siixERGSB9BaKDz74ACtWrMCQIUNKXb5nzx6jhSIiIsuht1BMmDABAHiaiYjoOae3UHh5eQEAdu3ahYULFxZbFhgYiE6dOhk3GRERWQS9hWLOnDlISUlBREQE0tLSpPkajQYJCQkmCUdEROant1AEBATg2rVruHr1Knx9faX5CoVCeo4EERE9+/QWilatWqFVq1bo2rUr3N3dTZmJiIgsiMHLY+fOnVvq/P/+979POgsREVkgg4Wi6GkntVqNgwcPSh3dRET07DNYKIYNG1ZiesyYMUYLRERElsXgEB6PEkIgNTXVGFmIiMgCGTyimDRpUrHp2NhY3kNhJPcf5CLqxn04pWTCvXoV1HVzNHckIqKK9VHIZDKMHDkS3bp1M2qo502+SouTF5MQE5cuzZPLAF+fBnijf3M42VubMR0RPe/K1UeRnp6O//3vf7CyskKbNm2gUChMke25kHg3B5sPJyAnT412Td3Qp2M9KK3kOBN1BwfCb+H4hSRMG9sBbT3dzB2ViJ5TBvsojh07hv79+2PdunVYvXo1Bg4ciHPnzpki2zMvIzMfIUfiIZcBw3s3gU+rWqhX0xEPslRo1sAFr/f1hEIuw5L1fyEhJdPccYnoOWWwUKxYsQIbN27Exo0bsXnzZgQHB2PRokWmyPZM02p1WLrxL+TmazGga0PUdKlSYp3qVe3wSvcXIJfLMDs4DHfTc82QlIiedwYLhUwmQ5MmTaTpli1bQghh1FDPg5CDMbj09z280rU2XJ3t9K5X1cEGU0e0Q06+Bl+sPwutVmfClEREZRSKjIwMZGRkwMvLC+vWrUN2djZyc3MREhKCLl26mDLjMycxNRM7jv6NPh3roZ1nNYPr13a1x9gBzREbn4Gf9l5BaloOMnNUJkhKRFRGZ3aXLl0gk8mko4elS5dKy2QyGaZNm2b8dM+on/degY1SgfGDWiIuIdHg+vlqLRQKOZrUc8Zvx6/D2koO3y4N4FiFV0MRkfHpLRQxMTGmzPHcuHzjPk5H3cEbA5rB2dEGcRV4b4+2dZB8NwuHz8WjT8f6RstIRFSU3kKxe/du+Pn54ccffyx1+Ztvvmm0UM8qIQR+/P0yXJxs4dejUYXfb2tthV7t62HvqZs4dCYO4we3NEJKIqLi9BaKuLiCv3VjY2NNFuZZdy46BVfj0jHlVW/YWhu8haVUDWo5oWFtJ/x+8iYGd3sBNcroCCciehL0flsFBgYCAGrUqIGPPvrIZIGeZb8e/Ruu1ezQp2O9x2qnW5va2HI4Fj/suYyPx3R4QumIiEpXrhvu6PHFxqfj8o37eKV7I1gpKjwWYzFO9jYY4NMAJ/6XhEt/331CCYmISmfw/EfdunXx1ltvoV27drC3t5fml6ePYs+ePVizZg00Gg3GjRuH0aNHF1u+atUq7NixA05OTgCA1157rcQ6z4qdx/6Gva0V+nV+Mp3QA3wa4MzlO/jvr5FY+VGvxy4+RET6GCwUzs7OAICkpKQKNZySkoLly5fj119/hbW1NUaMGIHOnTujcePG0jpRUVH46quv0LZt2wrGfrrcuZ+NsEvJGNqzMarYKp9Im9ZKBd4d2grzfziD30/ewNCejQ2/iYioEgwWij59+qBv377F5u3atctgw2FhYejSpYtUaHx9fXHgwAFMmTJFWicqKgrBwcFISkpCx44dMW3aNNjY2FT0M1i8PSduQCaTYUj3F55oux1b1ESH5jWx6eBV9GhbF0orOXLzNNJyO1sr3mtBRI9Nb6EIDQ2FRqPBkiVLIISQbrzTaDT45ptvMHTo0DIbTk1NhaurqzTt5uaGS5cuSdPZ2dlo3rw5goKC4OHhgenTp2P16tWYOnVqucNHRUWVe11zUWl0OHj6NprXs0Xc9Ssl7puwsnNGXPy/c+tUVxab1jevVlUtEm5mwKexDBeuavDlzyfwWp8XcDry3xv4urSqC01uxhP/TKYUERFh7ggWiftFP+4b/Sr7nam3UERHR+P06dO4f/8+1q9f/+8brKwwfvx4gw3rdDrIZDJpWghRbNre3h7fffedNP3WW29hxowZFSoUXl5eFn8EcvD0LeSrkzFmSHu0aFi9xPKLV67Do76HNO3g4FBsWt+8Wu5ucHMpuBcjNTcaW4/EYoDKtth6Rdd5GkVERKB9+/bmjmFxuF/0477RLyIiAl5eXpUqFnoLxeTJkzF58mSEhIRUqoPZ3d0df/31lzR99+5duLn9+0yF5ORkhIWFISAgAEBBIbGyqty9BZZKCIF9p26hQS0nNG/gYrTtBPRpgtCIBIQcjMGQbgWjzRIRPSkGL5Xp168f/vjjDwDAsmXLMG7cuHIN79G1a1eEh4cjLS0Nubm5OHToEHr06CEtt7W1xdKlS5GQkAAhBEJCQvDyyy8/xkexPFfj0nEj+QEGvtiw2NHUk2ZrbYV3XvFCYmoWom7cM9p2iOj5ZLBQfPLJJ0hISEB4eDiOHz8OPz8/fP755wYbrlmzJqZOnYqxY8di6NChGDx4MFq3bo0JEyYgMjISLi4umDdvHt577z30798fQohnbliQvWE3YWdjhV7t6hp9Wz6taqFFQxecuXwH2Xlqo2+PiJ4fBs/1ZGRkYPz48Vi8eDEGDx4Mf39/hISElKvxIUOGYMiQIcXmFe2X8PX1LfZM7mfJg6x8nPxfMny7eMDOxvin1GQyGUb7NsPsteH483wiBvg0MPo2iej5YPCIQq1WQ61W48SJE+jatStyc3ORk5NjimxPtSNn46HR6jCgawOTbdO9uj06e7njZvJDxMY/3Vc7EZHlMFgo+vTpAx8fH1SrVg1eXl549dVXMXjwYFNke2ppdQL7w2/Bq1F1eLg7mXTbbZq4wr16FZz4XxLSM/NMum0iejYZLBSBgYH4/fffpUtkly1bhsmTJxs92NPswtVUpKTlYGDXhibftlwmQ58O9aHR6rDut8t8dCoRPbZKPY8iPDz8met4fpL2nrqJao426OJVyyzbd3a0QY+2dXA0IhEb9kc/kedWZOaoit31DfDOb6LnBZ9H8YTduZ+NiJgUvNbXE0or8w3U16Jhdeh0AjuO/o0m9avhxda1H6u93DwNzl9NLTavXVM3Fgqi54DB51EsWrTIZGGeBfvDbkEmk8G3cwNzR8HIfs1w534Ovt58Hi6Otmje0Hg3/RHRs6vMP3kPHz6MESNGoG3btujatSvGjx+P48ePmyrbUyc3X4ODZ+Lg06oWXKuZ/8lzSis5Zr7ZCS5Otpj7fThi49PNHYmInkJ6C8X+/fuxdOlSvPHGG/jll1+wYcMG6Wa7Q4cOmTLjUyP0rwRk56rh19344ytptDqkpuUU+8lXa0usV83JFgveexFO9taYvTYcMbfSSqyTmaMq1k5mjsro+Yno6aH31NP69evx008/oXbtf89tN2rUCN7e3pgxYwb69etnkoBPC51OYM+J6/Cs74xmDaoZfXv5ai0u/V18uI6mHqVvt4azHRZMehGz/huGT1afxIShrTDAp4E0rMij/Q/seyCiovQeUWRnZxcrEoXMu1VvAAAZzklEQVQaNmyI/Px8o4Z6Gp2/moqku9l4pXsjo47rVFluLlXw1f/1QJsmrliz4xKWhUQg/SHvsyAiw/QeUSgUCr1vKnw2Bf1r95/X4eJkixfbPN7VRcbkUMUas9/ugq1HYrHtyFWcu5KCES97okPzmuaORkQW7Nka19tMYuLS8L9rdzF+UAuLf3a1XC7DyH5N0bNtHXz/WxR+/P0KthyOhWd9ZzRv4AIXJ1tzRyQiC6O3UFy9ehXt2rUrMV8IAZWKnZ1FbTl0FU721hj4ounvxK6s2q4OmP12F0TfTMP20Fj8FZ2Ci9fuwdnBBn8nPkCbxjXQqK6zdC9IaR3lRPR80FsoDh8+bMocT63Y+HRExKRi7MDmJhkl9klr3tAFE4e1RssXknAj6QGuJz3AkXPxOHQmDlYKOeq42qNeTUf0aFunxFMKiej5oPebrU6dOqbM8dTafOgqHKsoMegpOpooTRVbJbwa1YBXoxrwqOWIYxGJSEjJREJKFuLuJOPkxWQ42ClRr6YjGtd1Rl03B3NHJiITefr+BLYgV+PS8Fd0Ct4Y0AxVbJXmjvPE2FpboWHtqmhYuyoA4GG2CmqNFueupOB6Ygaib6XBzsYKsfHp6N+1AZrWr8YjDaJnGAtFJel0AsE7I+HiZIMh3V4wdxyjcrK3RlOPaqhe1Q4arQ7xdzIRG5+OPy8k4Y+/ElCrhj0Gdm2Ivp3qQwhRbPBADhxI9PRjoaikI+ficS0hAx+OavdMHU0YYqWQ44U6VfFCnapo5lENfyc+wKEzcVj3WxQ2HohGl5buqFXDHtWrFgxhwpv3iJ5+LBSVkJWrxvp9V9C8gYtJnodtqarYKtG3U3307VQf1xMzsPfUTRw7nwi1Roc6rg5o3aQGvJu4mjsmET0my77o30L9vPcKMrNVmOTf+rHPzc9aF4Vvt1+UfvaF3cTaXZH4ee8V7Dz2N85evoN9YTel6cKffWE3AUCaPnv5DuasDcfPe68AAM5evoPdx69L29l0MKbYdh+dPnv5jvTZ9oXdlNostGLrBZy9fAdnL98ptqywnUZ1nRH4elv06VAPPl618CArH/vDbmHiF0ew68/ryM5V690HRbMUfX300oNy7cNPvj1Z5vJHP+uTsulgTInshdPG2qaxVSS3sdZ9Eoyxvaf1d/oksFBU0KmLyTgQfgt+PRvjhTpVn3j7B8LjoNbokJWrRvK9bJyLTsGB8DhpuvDnQHjB80IKp89FpyA9Mx9Z/3whn4tOwW8nbkjtbj50tdh2Hp0+F50CoOBo6UB4nNRmoeuJD3AuOgXnolOKLXu0nQOn49CumRvGDGgOANAJYN1vUXhz/kGs3n4RsfHpJe7sL9pG0dd/RmWWa59F3bhf5vJHMz4pmw9dLZG9cNpY2zS2iuQ21rpPgjG297T+Tp8EnnqqgNv3srFy2wU0rV8NYwc2N3cciyaX/3uk9elbnfHHuXj8cS4e+8NvoV5NR/h28UCvdnVR1cHmsbZTONJtalqONM+YHeilPenvXkYOdEWeOFs0C9GzgIWinHLzNViy4RxkMhmCxnSw+KE6LEnawzy0beqGFi9Ux7WEDMTfeYjvd0fhp98vw6tRDQBASloOarpUqXDbhV/aphr9trQn/WXmqHE17t9nfTy6nOhpx0JRDrn5Gnz2/WncSH6ImW92qtQXGgE2SgW8XqiOsQOaIzdfg6MRCTjzT1/HOwsOw8PdEQBw9sodNK1v/KHaiah8WCgMyMvXYN6604i+eR//b3QHdGrhbu5IzwSPWk4YP7glxg9uiSEf7cbbr7TE2cspADIxf90Zab2vNkXAs3411Hd3RP2aTnB2fLxTVURUcSwUZbiZ/ABLN/6FpNQsfDiqPbq35bAmxjK0Z2MM7dkYQz7ajUXvv4iYuHT8vPcKLly9i6MRidJ6TvbWqFfTEXXdHOBe3V4aXytPpYGttfH/Oet0Ann5Gqi1/3ZK3Eh6gNv3sqVpTZFlRM8CFopS5Ku12HvyBjYeiIGDnRKfvesDb083c8eqlKIdq6lpOZDLIXW8Fi6r7MiwhV+IFe28LXyMa1FFO4TdqlWBe/Uq+HnvFSwL7I6MzHwk38vG3YwcpKbnIv5OJk5H3caDrH9HMV7322XYKBVwcrBG2KVkVHOyhbODjdRZ/ndCBmxtFLCzsYKttRVsrRUQANQaHVRqLTRaHfLVWjzMUiE9Mw/pmfnIyMxH2sM8pD/MR9rD3ILXmfl49HEsX2+5UGw6eGckAGDK0lBUc7KFq7MdPGo5oUk9Z9Sr6WhRNyCW1jmfmaMymLE8FxE82nZqWo7R79S3tXeWMj3u9krbN88roxaKPXv2YM2aNdBoNBg3bhxGjx5dbHl0dDRmzpyJ7OxsdOjQAZ999hmsrMxXu9Iz83AsIhG/HvsbGZn56NTCHYGvez/2lTnmVLRj9fzVVDT1qCZ1vBYu0/cIVUMKC0xFO29Le4xr0Q7hwpwAcCH2rrROr3b14FakfygnT43om2mY+/1pdG1dCw+zVXiYpULyvWxcuZlWrABO/frPin04ADJZwRGMi5MtXJxs0bB2VdhaK5CemQ+llVw60nlveGskpmRhz8mCy5E7t3THmct3IADE38lE1PX7xY4ynB1tUP+fo6L6NR1Rz90R9Wo6wtnBxuRjZpXWOZ+bp4FjFeuCoyeVBvkqLTRaAa1OB61OQKvV4W56LgBgf/gtKOQyKOQytG5cA+417GGlkENpJcfDLBUu/X1X+kznr6Ya/U59je7ff4+Pu73S9s3zymjfyikpKVi+fDl+/fVXWFtbY8SIEejcuTMaN24srRMUFITPP/9ceg73tm3bMGrUKGNFKkanE0jPzMPN5If4OzED/4u9iys370MIoHXjGpg2poN0RQ5Zpiq2BaPZAkDbIkd8hcVQrdEhJ0+NjQdiMPPNTsjL1yBXpUVevgZ5+RrI5TIoreRQWimgtJLDWilHVQcbODvYoJqTLaraW0PxyNVtqWk50pdHYaFo3sAF8iJf8B2a18SZy3ekMcCEEPBwd0JOvuafEXkzEZ+SiWPnE5FT5C/WwtF567s7oq5bQSFxdrCBo701HKsoYWdjVe5CIoRAvkqL7Dw1cvI0yMlTIztPg9w8DbLz1MjOVSM7T4176blIvJuFfJUWqn8K67RVJ5Cr0iI3Tw2dgYdZbg+9Jr3eVMp9BjIZoPxnH248EI3fTtyAg50SttYK2NoUHNnZWlvB5p//PjpfqZQDouDzCKDI0ZyAEIBOCOh0AjpR8P90XGI60nMK9lHU9XvIyMxHFTurgnV1hev+86MraFAul0Eul0Emk0Euk/0zDWTnqJF0NwsymQyFu/1YRAKslQrYWCtg889/rZX/vrb55/Wj/26edkYrFGFhYejSpQucnZ0BAL6+vjhw4ACmTJkCAEhKSkJeXh68vb0BAP7+/li5cmW5CkXhDVuVeYBSUmoWVu+4hHsZOdAW/l8gA+q6OuAN3yZo36ymNIS2KZ4N7myv/5GzhsihLfX9RefLoZW2I4cWWo1aWl64TKtRS9OPtlf0/Y8q+r6in+fR9xRdXvi+R9/zaK7S2taoVcjPL55Do1aVWK+wLRsrwMbBCs72CrRt4lLqPiyLRqOG5pEzDxq1qsTnK5q98PMWyyQrWLdRHQe0esFZaksIgYysfNy+l43ke1m4fS8HyfeycfnvFJyJTMSjFHIZ7GysoBNa2B47AoVMBrm84AtJq9NBo9VBq9VBrRXIV2shDH3LywpGClbIAaWVAg62CjjbK9C6UTU4O9qgiq0V7GyUsFHKoVDIoZAXbE8hB3JyNdhwIBq92teFEAJarUCtGg6wtVZAq9NBrRF4kJWP5HtZ0GgEYuLS0MC9oE9JCECl1uJBZj5S1TqoVBrkq3VSoXoSnO0VuBh7Bxdj7xheuQJtrtt9sVzrKuQyWCsL/gBRKhVQ/nOUpbSSQ6lQwEopg7Wi4I8UhaLgiKywIMlkMkAGyFEwrftn/xb8fgUgA/x6NKrU1ZeF35kVfZy1TBjpAdjBwcHIycnB1KlTAQC//PILLl26hPnz5wMALly4gCVLlmDz5s0AgLi4OLz77rs4ePCgwbYzMzMRGxtrjNhERM88T09PODo6lnt9ox1R6HS6YofJjz4dzdDystjb28PT0xNKpZLPQSAiKichBNRqNezt7Sv0PqMVCnd3d/z111/S9N27d+Hm5lZs+d27/3ZU3rt3r9jyssjl8gpVQyIiKmBra1vh9xitx6Vr164IDw9HWloacnNzcejQIfTo0UNaXqdOHdjY2CAiIgIAsHv37mLLiYjIMhitjwIouDw2ODgYarUaAQEBmDBhAiZMmIDAwEC0atUKMTExmDVrFrKystCyZUssWrQI1taWc405EREZuVAQEdHT79m62JeIiJ44FgoiIioTCwUREZWJhYKIiMrEQmEme/bswcCBA9GvXz+EhISYO45FWbVqFQYNGoRBgwZhyZIl5o5jcRYvXozp06ebO4bFCQ0Nhb+/PwYMGIDPP//c3HEsxu7du6X/nxYvXly5RgSZ3J07d0Tv3r1Fenq6yM7OFkOGDBHXrl0zdyyLcOrUKfH666+L/Px8oVKpxNixY8WhQ4fMHctihIWFic6dO4tp06aZO4pFiY+PF926dRO3b98WKpVKjBw5Uhw7dszcscwuJydHdOzYUdy/f1+o1WoREBAgTp06VeF2eERhBkUHTKxSpYo0YCIBrq6umD59OqytraFUKtGoUSMkJyebO5ZFyMjIwPLlyzFp0iRzR7E4hw8fxsCBA+Hu7g6lUonly5ejTZs25o5ldlqtFjqdDrm5udBoNNBoNLCxqfhjE1gozCA1NRWurq7StJubG1JSUsyYyHI0adJEGlH41q1b2L9/P3r27GnmVJZh9uzZmDp1KpycnMwdxeLExcVBq9Vi0qRJ8PPzw6ZNm1C1alVzxzI7BwcHfPDBBxgwYAB69uyJOnXqoF27dhVuh4XCDB5nQMTnxbVr1/DWW2/h448/RoMGDcwdx+x++eUX1KpVCz4+PuaOYpG0Wi3Cw8OxcOFCbN26FZcuXcLOnTvNHcvsYmJisGPHDhw9ehQnTpyAXC7HunXrKtwOC4UZPDog4qMDJj7vIiIiMH78eHz00UcYNmyYueNYhH379uHUqVPw8/PDypUrERoaioULF5o7lsWoUaMGfHx84OLiAltbW/Tt2xeXLl0ydyyzO3nyJHx8fFC9enVYW1vD398fZ8+erXA7LBRmYGjAxOfZ7du3MXnyZCxbtgyDBg0ydxyL8eOPP+L333/H7t27ERgYiJdeegkzZswwdyyL0bt3b5w8eRIPHz6EVqvFiRMn0LJlS3PHMrtmzZohLCwMOTk5EEIgNDQUrVq1qnA75ntA9XOsZs2amDp1KsaOHSsNmNi6dWtzx7II69atQ35+Pr744gtp3ogRIzBy5EgzpiJL16ZNG7zzzjsYNWoU1Go1XnzxRQwfPtzcscyuW7duuHLlCvz9/aFUKtGqVSu8++67FW6HgwISEVGZeOqJiIjKxEJBRERlYqEgIqIysVAQEVGZWCiIiKhMLBRUpsTERDRt2hRvvPFGiWXTp09H06ZNkZaWVun2V61ahSNHjkjt6btr9HG38yStWLECu3btKnOdP/74QxrB9NixY1ixYoXBdouuV/T9T8qmTZuwdetWpKSkYMSIEZVu56WXXkJkZOQTyRQVFYVPP/30ibRFxsP7KMggGxsb3Lx5E0lJSahTpw4AICcnB+fPn3/sts+cOYPGjRs/djum9MEHHxhcp0+fPujTpw8AIDIyEg8ePDD4nqLrFX3/k5CUlISdO3di27ZtkMlk2LJlyxNr+3F4eXkhJCQER48eRe/evc0dh/RgoSCDFAoFBgwYgD179kgjlx46dAh9+vTBDz/8IK23detWbNiwAXK5HDVq1MCnn36Khg0bYvr06XBwcMDVq1dx584dNG3aFIsXL8auXbsQFRWFJUuWQKFQAAAuXLiAESNG4N69e2jSpAm+/PJLVKlSRdrGm2++iQEDBuC1114DAKxevRoZGRkl7lJu0aIFJkyYgBMnTiAnJwcffvgh+vXrh19//RXbt29Hbm4uHBwcsGHDBvzyyy/YvHkzdDodnJ2d8emnn6JRo0bIzs7G559/jvPnz0OhUKBv376YOnUqPvnkEzRp0gRvv/12mds5ePAg3n//fWzZsgVarRaOjo6YOHEi5s6di7i4OGRkZMDe3h7Lli1DZmZmsfU8PDxw8OBBBAcH486dO5g7dy6SkpIghMDQoUPxzjvvIDExEePHj0fPnj1x8eJFPHz4EEFBQXj55ZdL/A6Dg4Ph5+cHmUyGxMREDBkyBBcuXMA333yDpKQk3L17F0lJSahZsyaWLl0KNzc33Lx5E7Nnz0ZaWhrkcjnee+89DBw4UPpdz5kzB2lpafDz88PUqVMBFDwTYs2aNVCr1bC1tcW0adPQtm1bXL9+HTNnzoRKpYIQAgEBARg9ejQA4PXXX8fcuXNZKCzZEx38nJ45CQkJwtvbW0RGRor+/ftL88eNGyeuXr0qPD09xf3790VYWJjo27evuH//vhBCiB07dogBAwYInU4npk2bVuwZE0OHDhXbt28XQgjxxhtviP379wshhJg2bZoICAgQOTk5QqPRiGHDhomdO3cKIYS0ncOHD4vhw4cLIYTQarWid+/e4vr16yVye3p6ijVr1gghhIiOjhbt27cX9+/fFzt27BAdO3YUmZmZQgghzpw5I0aNGiVycnKEEEKcOHFC+pwLFy4UU6dOFRqNRuTn54vRo0eL06dPi2nTponvv//e4HbeffddIYQQK1euFJ999pkQQoj9+/eL+fPnSzk//fRTMW/evBLrFX3/6NGjxQ8//CCEEOLhw4diyJAh4vfffxcJCQnC09NThIaGCiGEOHDggOjVq1eJfaHT6UTnzp1FQkJCsd9p4Tb79Okj7Y+JEyeKFStWCCGEGDp0qNi4caMQQojk5GRpvd69e0uZU1NThZeXl0hOThY3b94UgwcPFmlpaUIIIWJjY8WLL74osrOzxSeffCKCg4Ol9/zf//2f0Gq1Usa2bduK+Pj4EtnJMvCIgsrFy8sLCoUCUVFRqF69OrKzs+Hp6SktP3HiBAYOHAgXFxcAgL+/PxYsWIDExEQAQPfu3WFtbQ0A8PT01Hsqpm/fvrCzswNQMOT4o/0SvXv3xoIFCxATE4OUlBTUrVsXL7zwQqltFfarNGvWDJ6enjh37hyAgv4OBwcHAAX9AnFxccXO2T98+BAZGRkICwvDJ598AoVCAYVCgY0bNwJAiVFJ9W2nNP3790e9evWwYcMGxMXF4ezZs2jbtq3e9QtP8RUeuTk6OsLf3x/Hjx9HmzZtoFQqpWHYW7RogYyMjBJtpKenIzMzE3Xr1i11G506dZL2R4sWLfDgwQNkZGQgJiYGr776KgCgVq1aUl8SAAwePBhAwfNDatSogfv37+PixYtITU3F+PHjpfVkMhni4+Px8ssvY9q0abh06RJ8fHwwa9YsyOX/dpHWrVsXN2/eRL169fTuCzIfFgoqt1deeQW//fYbXFxc4OfnV2yZTqcrsb4QAhqNBgBga2srzZfJZBB6Ro6xsrIqcz2FQoHXX38d27dvR2pqapmdsoWnswrzFU4XPZWl0+ng5+eHoKAgaTo1NRVVq1aFlZVVseHfb9++XexzGNpOaTZt2oRt27Zh9OjRGDJkCJydnaViWhqdTldiH+h0Omm/KpVK6QtX31D1hftRp9MV+3IuVNrvpvD3ULTNGzduoHbt2gBK/z3pdDr4+Pjg66+/lpbdvn0bbm5uaNasGQ4ePIiwsDCEh4fj22+/xa+//gp3d3epvbL2G5kXr3qicvPz88OBAwewb98+6S/KQt27d8e+ffukI4AdO3bA2dkZHh4eZbapUCikL73yevXVV3HkyBFcvny51PPxhQqvTLp8+TJu3ryJjh07llinW7du2Lt3L1JTUwEAmzdvxrhx4wAAPj4+2LlzJ3Q6HVQqFQIDA0s9WjC0naKf8eTJkxg2bBheffVVNGzYEKGhodBqtXr3hYODA9q0aSM9Vz0zMxO7du1C165dDe+of1SrVg1OTk5ISkoq93scHBzQsmVL6bPdvn0bI0eORGZmpt73+Pj44NSpU7h+/ToA4M8//8Qrr7yCvLw8fPTRR9i3bx8GDRqEOXPmwMHBAfHx8QAK/qBITk5Gw4YNy52PTItHFFRuNWvWRKNGjeDo6AhnZ+diy1588UWMHz8e48aNg06ng4uLC4KDg0v9C7aol156CV999RXUanW5c1SvXh1eXl5o1KgRlEql3vXOnz+Pbdu2QafTYfny5aU+8axbt26YMGEC3nrrLchkMjg4OGDVqlWQyWSYMmUKFixYAD8/P2i1WgwcOBD9+vVDaGhohbbTpUsX/L//9/8wf/58vPXWW5g9eza2b98OAPD29kZsbGyJ9YoOkb1s2TLMmzcPv/76K1QqFYYMGQJ/f/8KffH369cPJ06cwKhRo8r9ni+//BKfffYZNmzYAJlMhgULFhR7MuOjGjdujHnz5uHDDz+UjkrWrFkDe3t7vP/++5g5cya2bt0qXRhQWFAjIyNRv3596WiFLA9Hj6WnTlpaGgICAhASEoJatWqVuk7Tpk0RHh4u9ZkYi6m287gSEhLwwQcfYMeOHRb3NMXp06ejf//+6NWrl7mjkB489URPlW3btmHgwIF4++239RYJKqlevXoYOnSoxdw/USgqKgoymYxFwsLxiIKIiMrEIwoiIioTCwUREZWJhYKIiMrEQkFERGVioSAiojKxUBARUZn+P/iSyB3twgTKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn distplot\n",
    "sns.distplot(df.groupby(['YEAR', 'MONTH']).sum().PRCP, color='b', bins=50, rug=True, kde=True)\n",
    "plt.xlabel('Monthly precipitation (inches)')\n",
    "plt.ylabel('Distribution of months (not counts)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>Here we are plotting the sum for each month using groupby and sum. Distplot gives us the histogram plus a kernel density estimate and rugplot.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "D3. Use groupby to group the data by year or by month. Which year was the rainiest? Which month was the rainiest?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>NAME</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>ELEVATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>PRCP_ATTRIBUTES</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>2009-02-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>2009-02-19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>2009-02-20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>2009-02-21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US1CASD0030</td>\n",
       "      <td>LA JOLLA 2.1 SE, CA US</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>2009-02-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>,,N</td>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       STATION                    NAME  LATITUDE  LONGITUDE  ELEVATION  \\\n",
       "0  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "1  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "2  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "3  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "4  US1CASD0030  LA JOLLA 2.1 SE, CA US   32.8257  -117.2501      149.7   \n",
       "\n",
       "        DATE  PRCP PRCP_ATTRIBUTES  YEAR  MONTH  DAY  DAY_OF_YEAR  \n",
       "0 2009-02-18   0.0             ,,N  2009      2   18           49  \n",
       "1 2009-02-19   0.0             ,,N  2009      2   19           50  \n",
       "2 2009-02-20   0.0             ,,N  2009      2   20           51  \n",
       "3 2009-02-21   0.0             ,,N  2009      2   21           52  \n",
       "4 2009-02-22   0.0             ,,N  2009      2   22           53  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We added 'YEAR' and 'MONTH' above. We also added 'DAY' and 'DAY_OF_YEAR'\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>ELEVATION</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.012755</td>\n",
       "      <td>6.501377</td>\n",
       "      <td>15.688705</td>\n",
       "      <td>182.214876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.013439</td>\n",
       "      <td>7.321656</td>\n",
       "      <td>16.006369</td>\n",
       "      <td>207.334395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.015595</td>\n",
       "      <td>5.655949</td>\n",
       "      <td>15.418006</td>\n",
       "      <td>156.167203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.022857</td>\n",
       "      <td>6.510989</td>\n",
       "      <td>15.678571</td>\n",
       "      <td>182.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.026022</td>\n",
       "      <td>6.565826</td>\n",
       "      <td>15.647059</td>\n",
       "      <td>184.137255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.027775</td>\n",
       "      <td>6.541209</td>\n",
       "      <td>15.760989</td>\n",
       "      <td>183.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.028000</td>\n",
       "      <td>6.477778</td>\n",
       "      <td>15.627778</td>\n",
       "      <td>182.272222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.035656</td>\n",
       "      <td>6.513661</td>\n",
       "      <td>15.756831</td>\n",
       "      <td>183.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.037022</td>\n",
       "      <td>6.463483</td>\n",
       "      <td>15.966292</td>\n",
       "      <td>181.331461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.057214</td>\n",
       "      <td>6.523677</td>\n",
       "      <td>15.841226</td>\n",
       "      <td>183.036212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YEAR  LATITUDE  LONGITUDE  ELEVATION      PRCP     MONTH        DAY  \\\n",
       "4  2013   32.8257  -117.2501      149.7  0.012755  6.501377  15.688705   \n",
       "0  2009   32.8257  -117.2501      149.7  0.013439  7.321656  16.006369   \n",
       "9  2018   32.8257  -117.2501      149.7  0.015595  5.655949  15.418006   \n",
       "5  2014   32.8257  -117.2501      149.7  0.022857  6.510989  15.678571   \n",
       "2  2011   32.8257  -117.2501      149.7  0.026022  6.565826  15.647059   \n",
       "6  2015   32.8257  -117.2501      149.7  0.027775  6.541209  15.760989   \n",
       "3  2012   32.8257  -117.2501      149.7  0.028000  6.477778  15.627778   \n",
       "7  2016   32.8257  -117.2501      149.7  0.035656  6.513661  15.756831   \n",
       "8  2017   32.8257  -117.2501      149.7  0.037022  6.463483  15.966292   \n",
       "1  2010   32.8257  -117.2501      149.7  0.057214  6.523677  15.841226   \n",
       "\n",
       "   DAY_OF_YEAR  \n",
       "4   182.214876  \n",
       "0   207.334395  \n",
       "9   156.167203  \n",
       "5   182.500000  \n",
       "2   184.137255  \n",
       "6   183.500000  \n",
       "3   182.272222  \n",
       "7   183.500000  \n",
       "8   181.331461  \n",
       "1   183.036212  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['YEAR'], as_index=False).mean().sort_values('PRCP')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>Answer: The rainiest year was 2010.</span> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MONTH</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>ELEVATION</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.001194</td>\n",
       "      <td>2013.500000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>228.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.002391</td>\n",
       "      <td>2013.545455</td>\n",
       "      <td>15.444444</td>\n",
       "      <td>166.646465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.006097</td>\n",
       "      <td>2013.500000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>197.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.007930</td>\n",
       "      <td>2013.456140</td>\n",
       "      <td>15.954386</td>\n",
       "      <td>259.164912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.017143</td>\n",
       "      <td>2013.516234</td>\n",
       "      <td>15.912338</td>\n",
       "      <td>136.113636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.018567</td>\n",
       "      <td>2013.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>105.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.019935</td>\n",
       "      <td>2013.470779</td>\n",
       "      <td>16.016234</td>\n",
       "      <td>289.217532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.031656</td>\n",
       "      <td>2013.516234</td>\n",
       "      <td>15.964286</td>\n",
       "      <td>75.165584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.032419</td>\n",
       "      <td>2013.162455</td>\n",
       "      <td>15.115523</td>\n",
       "      <td>319.332130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.055097</td>\n",
       "      <td>2013.857143</td>\n",
       "      <td>14.957529</td>\n",
       "      <td>45.957529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.069388</td>\n",
       "      <td>2013.996403</td>\n",
       "      <td>16.053957</td>\n",
       "      <td>16.053957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.086715</td>\n",
       "      <td>2013.010949</td>\n",
       "      <td>15.806569</td>\n",
       "      <td>350.018248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MONTH  LATITUDE  LONGITUDE  ELEVATION      PRCP         YEAR        DAY  \\\n",
       "7       8   32.8257  -117.2501      149.7  0.001194  2013.500000  16.000000   \n",
       "5       6   32.8257  -117.2501      149.7  0.002391  2013.545455  15.444444   \n",
       "6       7   32.8257  -117.2501      149.7  0.006097  2013.500000  16.000000   \n",
       "8       9   32.8257  -117.2501      149.7  0.007930  2013.456140  15.954386   \n",
       "4       5   32.8257  -117.2501      149.7  0.017143  2013.516234  15.912338   \n",
       "3       4   32.8257  -117.2501      149.7  0.018567  2013.500000  15.500000   \n",
       "9      10   32.8257  -117.2501      149.7  0.019935  2013.470779  16.016234   \n",
       "2       3   32.8257  -117.2501      149.7  0.031656  2013.516234  15.964286   \n",
       "10     11   32.8257  -117.2501      149.7  0.032419  2013.162455  15.115523   \n",
       "1       2   32.8257  -117.2501      149.7  0.055097  2013.857143  14.957529   \n",
       "0       1   32.8257  -117.2501      149.7  0.069388  2013.996403  16.053957   \n",
       "11     12   32.8257  -117.2501      149.7  0.086715  2013.010949  15.806569   \n",
       "\n",
       "    DAY_OF_YEAR  \n",
       "7    228.200000  \n",
       "5    166.646465  \n",
       "6    197.200000  \n",
       "8    259.164912  \n",
       "4    136.113636  \n",
       "3    105.700000  \n",
       "9    289.217532  \n",
       "2     75.165584  \n",
       "10   319.332130  \n",
       "1     45.957529  \n",
       "0     16.053957  \n",
       "11   350.018248  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['MONTH'], as_index=False).mean().sort_values('PRCP')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>Answer: The rainiest month was December.</span> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "D4. Make boxplots by year and by month using the Seaborn function `boxplot()`. Hint: If you make a boxplot of your DataFrame without grouping, the boxplots will be centered on zero, because there are so many days with zero precipitation. Instead, use groupby to group the data by year *and* month (use a list containing these columns), set `as_index=False`, average over those groups, and save this as a new DataFrame; then use this for your boxplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_mean_by_month = df.groupby(['YEAR', 'MONTH'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot by month\n",
    "sns.boxplot(x='MONTH', y='PRCP', data=df_mean_by_month)\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Precipitation (inches)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>The distribution shows December was the wettest month (daily mean) overall. But it appears the wettest single month was in Feburary.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot by year\n",
    "sns.boxplot(x='YEAR', y='PRCP', data=df_mean_by_month)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Precipitation (inches)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>The distribution shows 2010, 2015, and 2018 had the higest median month precipitation (daily mean). However, 2018 has only January and February, which are wet months! Based on the outliers, 2010, 2016, and 2017 had the wettest months.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>ELEVATION</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.216429</td>\n",
       "      <td>14.5</td>\n",
       "      <td>45.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.214516</td>\n",
       "      <td>16.0</td>\n",
       "      <td>350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.169677</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>32.8257</td>\n",
       "      <td>-117.2501</td>\n",
       "      <td>149.7</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>351.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YEAR  MONTH  LATITUDE  LONGITUDE  ELEVATION      PRCP   DAY  DAY_OF_YEAR\n",
       "96  2017      2   32.8257  -117.2501      149.7  0.216429  14.5         45.5\n",
       "22  2010     12   32.8257  -117.2501      149.7  0.214516  16.0        350.0\n",
       "11  2010      1   32.8257  -117.2501      149.7  0.169677  16.0         16.0\n",
       "95  2017      1   32.8257  -117.2501      149.7  0.160000  16.0         16.0\n",
       "94  2016     12   32.8257  -117.2501      149.7  0.150000  16.0        351.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean_by_month.sort_values('PRCP', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>Sorting our new DataFrame by precipitation and viewing the top few rows, we can see that the wettest two months were February 2017 and December 2010.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer to Question E - Pivoting, stacking and unstacking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "E1. Use pivot_table to produce a new DataFrame, where rows=years and columns=months, containing the mean precipitation of each month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>MONTH</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEAR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007742</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>0.006774</td>\n",
       "      <td>0.003704</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>0.001333</td>\n",
       "      <td>0.001290</td>\n",
       "      <td>0.011333</td>\n",
       "      <td>0.101613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0.169677</td>\n",
       "      <td>0.088462</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.064333</td>\n",
       "      <td>0.001935</td>\n",
       "      <td>0.002667</td>\n",
       "      <td>0.003548</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001923</td>\n",
       "      <td>0.080968</td>\n",
       "      <td>0.032333</td>\n",
       "      <td>0.214516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.027742</td>\n",
       "      <td>0.061538</td>\n",
       "      <td>0.022414</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.025517</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001429</td>\n",
       "      <td>0.018065</td>\n",
       "      <td>0.108000</td>\n",
       "      <td>0.039032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.018065</td>\n",
       "      <td>0.057778</td>\n",
       "      <td>0.041613</td>\n",
       "      <td>0.053333</td>\n",
       "      <td>0.000645</td>\n",
       "      <td>0.001667</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>0.001613</td>\n",
       "      <td>0.001333</td>\n",
       "      <td>0.042258</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.121852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.047419</td>\n",
       "      <td>0.017143</td>\n",
       "      <td>0.038065</td>\n",
       "      <td>0.006000</td>\n",
       "      <td>0.016452</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.001290</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011613</td>\n",
       "      <td>0.002857</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.002258</td>\n",
       "      <td>0.037500</td>\n",
       "      <td>0.059032</td>\n",
       "      <td>0.018000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011290</td>\n",
       "      <td>0.006452</td>\n",
       "      <td>0.018000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019333</td>\n",
       "      <td>0.105333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.013571</td>\n",
       "      <td>0.032903</td>\n",
       "      <td>0.005333</td>\n",
       "      <td>0.040645</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.044194</td>\n",
       "      <td>0.001290</td>\n",
       "      <td>0.035667</td>\n",
       "      <td>0.033226</td>\n",
       "      <td>0.067333</td>\n",
       "      <td>0.038710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.107419</td>\n",
       "      <td>0.015517</td>\n",
       "      <td>0.035161</td>\n",
       "      <td>0.018333</td>\n",
       "      <td>0.033226</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000968</td>\n",
       "      <td>0.015667</td>\n",
       "      <td>0.004839</td>\n",
       "      <td>0.042667</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.216429</td>\n",
       "      <td>0.008065</td>\n",
       "      <td>0.004667</td>\n",
       "      <td>0.036129</td>\n",
       "      <td>0.009667</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>0.000968</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005667</td>\n",
       "      <td>0.004516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.073548</td>\n",
       "      <td>0.013929</td>\n",
       "      <td>0.050968</td>\n",
       "      <td>0.002667</td>\n",
       "      <td>0.010645</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006207</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "MONTH        1         2         3         4         5         6         7   \\\n",
       "YEAR                                                                          \n",
       "2009        NaN  0.000000  0.007742  0.003000  0.006774  0.003704  0.000000   \n",
       "2010   0.169677  0.088462  0.020000  0.064333  0.001935  0.002667  0.003548   \n",
       "2011   0.027742  0.061538  0.022414  0.010000  0.025517  0.003000  0.000000   \n",
       "2012   0.018065  0.057778  0.041613  0.053333  0.000645  0.001667  0.000323   \n",
       "2013   0.047419  0.017143  0.038065  0.006000  0.016452  0.000333  0.001290   \n",
       "2014   0.002258  0.037500  0.059032  0.018000  0.000000  0.000000  0.011290   \n",
       "2015   0.016667  0.013571  0.032903  0.005333  0.040645  0.002000  0.044194   \n",
       "2016   0.107419  0.015517  0.035161  0.018333  0.033226  0.000667  0.000000   \n",
       "2017   0.160000  0.216429  0.008065  0.004667  0.036129  0.009667  0.000323   \n",
       "2018   0.073548  0.013929  0.050968  0.002667  0.010645  0.000333  0.000000   \n",
       "\n",
       "MONTH        8         9         10        11        12  \n",
       "YEAR                                                     \n",
       "2009   0.000323  0.001333  0.001290  0.011333  0.101613  \n",
       "2010   0.000000  0.001923  0.080968  0.032333  0.214516  \n",
       "2011   0.000000  0.001429  0.018065  0.108000  0.039032  \n",
       "2012   0.001613  0.001333  0.042258  0.010000  0.121852  \n",
       "2013   0.000323  0.000000  0.011613  0.002857  0.010000  \n",
       "2014   0.006452  0.018000  0.000000  0.019333  0.105333  \n",
       "2015   0.001290  0.035667  0.033226  0.067333  0.038710  \n",
       "2016   0.000968  0.015667  0.004839  0.042667  0.150000  \n",
       "2017   0.000968  0.000476  0.000000  0.005667  0.004516  \n",
       "2018   0.000000  0.000000  0.006207  0.000000       NaN  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using pivot_table to get the mean of each month in a table where rows=years and columns=months\n",
    "precip = df.pivot_table(index='YEAR', columns='MONTH', values='PRCP', aggfunc='mean')\n",
    "precip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "E2. Draw a heatmap of years x months where each square is a month colored by mean precipitation. Adjust the colormap to highlight months with heavy precipitation. Hint: Seaborn's `heatmap()` function makes this very easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a21093cf8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap colored by precipitation\n",
    "fig, ax = plt.subplots(figsize=(9, 6))\n",
    "sns.heatmap(precip, annot=False, fmt='f', linewidths=.5, ax=ax, cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "E3. Stack the monthly precipitation table using `stack()`. View the values for 2017. Which month had the most precipitation in 2017? Describe the distribution statistics for all months using `describe()`. How many months are included in this dataset? What was the median month (daily average) value in this time period?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stack precipitation pivot table by year-month indexes\n",
    "precip_stack = precip.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MONTH\n",
       "1     0.160000\n",
       "2     0.216429\n",
       "3     0.008065\n",
       "4     0.004667\n",
       "5     0.036129\n",
       "6     0.009667\n",
       "7     0.000323\n",
       "8     0.000968\n",
       "9     0.000476\n",
       "10    0.000000\n",
       "11    0.005667\n",
       "12    0.004516\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# daily average for 2017, month by month\n",
    "precip_stack[2017]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>February had the most precipitation of any month in 2017.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    118.000000\n",
       "mean       0.027751\n",
       "std        0.042921\n",
       "min        0.000000\n",
       "25%        0.001333\n",
       "50%        0.010000\n",
       "75%        0.037157\n",
       "max        0.216429\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# distribution statistics for mean daily averages by month\n",
    "precip_stack.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>There are 109 months in this dataset. The median month (daily average) value was 0.011 inches of precipitation.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "E4. Use `unstack()` to stack precipitation pivot table by month and then year. View the values for December. Which year had the wettest December?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (un)stack precipitation pivot table by month-year indexes\n",
    "precip_unstack = precip.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YEAR\n",
       "2009    0.101613\n",
       "2010    0.214516\n",
       "2011    0.039032\n",
       "2012    0.121852\n",
       "2013    0.010000\n",
       "2014    0.105333\n",
       "2015    0.038710\n",
       "2016    0.150000\n",
       "2017    0.004516\n",
       "2018         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# daily average for December, year by year\n",
    "precip_unstack[12]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='color:red'>The wettest December (by daily average) was in 2010.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Postscript:** Read more about [San Diego's odd 2010 weather](http://www.sandiegouniontribune.com/sdut-highlights-san-diegos-wild-and-weird-2010-weather-2011jan02-htmlstory.html)."
   ]
  }
 ],
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